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How To Fine-Tune Segment Anything
Computer vision is having its ChatGPT moment with the release of the Segment Anything Model (SAM) by Meta last week. Trained over 11 billion segmentation masks, SAM is a foundation model for predictive AI use cases rather than generative AI. While it has shown an incredible amount of flexibility in its ability to segment over wide-ranging image modalities and problem spaces, it was released without “fine-tuning” functionality. This tutorial will outline some of the key steps to fine-tune SAM using the mask decoder, particularly describing which functions from SAM to use to pre/post-process the data so that it's in good shape for fine-tuning. {{Training_data_CTA::Supercharge your annotations by fine-tuning SAM for your use case}} What is the Segment Anything Model (SAM)? The Segment Anything Model (SAM) is a segmentation model developed by Meta AI. It is considered the first foundational model for Computer Vision. SAM was trained on a huge corpus of data containing millions of images and billions of masks, making it extremely powerful. As its name suggests, SAM is able to produce accurate segmentation masks for a wide variety of images. SAM’s design allows it to take human prompts into account, making it particularly powerful for Human In The Loop annotation. These prompts can be multi-modal: they can be points on the area to be segmented, a bounding box around the object to be segmented, or a text prompt about what should be segmented. The model is structured into 3 components: an image encoder, a prompt encoder, and a mask decoder. Source The image encoder generates an embedding for the image being segmented, whilst the prompt encoder generates an embedding for the prompts. The image encoder is a particularly large component of the model. This is in contrast to the lightweight mask decoder, which predicts segmentation masks based on the embeddings. Meta AI has made the weights and biases of the model trained on the Segment Anything 1 Billion Mask (SA-1B) dataset available as a model checkpoint. {{light_callout_start}} Learn more about how Segment Anything works in our explainer blog post Segment Anything Model (SAM) Explained. {{light_callout_end}} What is Model Fine-Tuning? Publicly available state-of-the-art models have a custom architecture and are typically supplied with pre-trained model weights. If these architectures were supplied without weights then the models would need to be trained from scratch by the users, who would need to use massive datasets to obtain state-of-the-art performance. Model fine-tuning is the process of taking a pre-trained model (architecture+weights) and showing it data for a particular use case. This will typically be data that the model hasn’t seen before, or that is underrepresented in its original training dataset. The difference between fine-tuning the model and starting from scratch is the starting value of the weights and biases. If we were training from scratch, these would be randomly initialized according to some strategy. In such a starting configuration, the model would ‘know nothing’ of the task at hand and perform poorly. By using pre-existing weights and biases as a starting point we can ‘fine tune’ the weights and biases so that our model works better on our custom dataset. For example, the information learned to recognize cats (edge detection, counting paws) will be useful for recognizing dogs. Why Would I Fine-Tune a Model? The purpose of fine-tuning a model is to obtain higher performance on data that the pre-trained model has not seen before. For example, an image segmentation model trained on a broad corpus of data gathered from phone cameras will have mostly seen images from a horizontal perspective. If we tried to use this model for satellite imagery taken from a vertical perspective, it may not perform as well. If we were trying to segment rooftops, the model may not yield the best results. The pre-training is useful because the model will have learned how to segment objects in general, so we want to take advantage of this starting point to build a model that can accurately segment rooftops. Furthermore, it is likely that our custom dataset would not have millions of examples, so we want to fine-tune instead of training the model from scratch. Fine tuning is desirable so that we can obtain better performance on our specific use case, without having to incur the computational cost of training a model from scratch. How to Fine-Tune Segment Anything Model [With Code] Background & Architecture We gave an overview of the SAM architecture in the introduction section. The image encoder has a complex architecture with many parameters. In order to fine-tune the model, it makes sense for us to focus on the mask decoder which is lightweight and therefore easier, faster, and more memory efficient to fine-tune. In order to fine-tune SAM, we need to extract the underlying pieces of its architecture (image and prompt encoders, mask decoder). We cannot use SamPredictor.predict (link) for two reasons: We want to fine-tune only the mask decoder This function calls SamPredictor.predict_torch which has the @torch.no_grad() decorator (link), which prevents us from computing gradients Thus, we need to examine the SamPredictor.predict function and call the appropriate functions with gradient calculation enabled on the part we want to fine-tune (the mask decoder). Doing this is also a good way to learn more about how SAM works. Creating a Custom Dataset We need three things to fine-tune our model: Images on which to draw segmentations Segmentation ground truth masks Prompts to feed into the model We chose the stamp verification dataset (link) since it has data that SAM may not have seen in its training (i.e., stamps on documents). We can verify that it performs well, but not perfectly, on this dataset by running inference with the pre-trained weights. The ground truth masks are also extremely precise, which will allow us to calculate accurate losses. Finally, this dataset contains bounding boxes around the segmentation masks, which we can use as prompts to SAM. An example image is shown below. These bounding boxes align well with the workflow that a human annotator would go through when looking to generate segmentations. Input Data Preprocessing We need to preprocess the scans from numpy arrays to pytorch tensors. To do this, we can follow what happens inside SamPredictor.set_image (link) and SamPredictor.set_torch_image (link) which preprocesses the image. First, we can use utils.transform.ResizeLongestSide to resize the image, as this is the transformer used inside the predictor (link). We can then convert the image to a pytorch tensor and use the SAM preprocess method (link) to finish preprocessing. Training Setup We download the model checkpoint for the vit_b model and load them in: sam_model = sam_model_registry['vit_b'](checkpoint='sam_vit_b_01ec64.pth') We can set up an Adam optimizer with defaults and specify that the parameters to tune are those of the mask decoder: optimizer = torch.optim.Adam(sam_model.mask_decoder.parameters()) At the same time, we can set up our loss function, for example Mean Squared Error loss_fn = torch.nn.MSELoss() Training Loop In the main training loop, we will be iterating through our data items, generating masks, and comparing them to our ground truth masks so that we can optimize the model parameters based on the loss function. In this example, we used a GPU for training since it is much faster than using a CPU. It is important to use .to(device) on the appropriate tensors to make sure that we don’t have certain tensors on the CPU and others on the GPU. We want to embed images by wrapping the encoder in the torch.no_grad() context manager, since otherwise we will have memory issues, along with the fact that we are not looking to fine-tune the image encoder. with torch.no_grad(): image_embedding = sam_model.image_encoder(input_image) We can also generate the prompt embeddings within the no_grad context manager. We use our bounding box coordinates, converted to pytorch tensors. with torch.no_grad(): sparse_embeddings, dense_embeddings = sam_model.prompt_encoder( points=None, boxes=box_torch, masks=None, ) Finally, we can generate the masks. Note that here we are in single mask generation mode (in contrast to the 3 masks that are normally output). low_res_masks, iou_predictions = sam_model.mask_decoder( image_embeddings=image_embedding, image_pe=sam_model.prompt_encoder.get_dense_pe(), sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=False, ) The final step here is to upscale the masks back to the original image size since they are low resolution. We can use Sam.postprocess_masks to achieve this. We will also want to generate binary masks from the predicted masks so that we can compare these to our ground truths. It is important to use torch functionals in order to not break backpropagation. upscaled_masks = sam_model.postprocess_masks(low_res_masks, input_size, original_image_size).to(device) from torch.nn.functional import threshold, normalize binary_mask = normalize(threshold(upscaled_masks, 0.0, 0)).to(device) Finally, we can calculate the loss and run an optimization step: loss = loss_fn(binary_mask, gt_binary_mask) optimizer.zero_grad() loss.backward() optimizer.step() By repeating this over a number of epochs and batches we can fine-tune the SAM decoder. Saving Checkpoints and Starting a Model from it Once we are done with training and satisfied with the performance uplift, we can save the state dict of the tuned model using: torch.save(model.state_dict(), PATH) We can then load this state dict when we want to perform inference on data that is similar to the data we used to fine-tune the model. {{light_callout_start}} You can find the Colab Notebook with all the code you need to fine-tune SAM here. Keep reading if you want a fully working solution out of the box! {{light_callout_end}} Fine-Tuning for Downstream Applications While SAM does not currently offer fine-tuning out of the box, we are building a custom fine-tuner integrated with the Encord platform. As shown in this post, we fine-tune the decoder in order to achieve this. This is available as an out-of-the-box one-click procedure in the web app, where the hyperparameters are automatically set. Original vanilla SAM mask: Mask generated by fine-tuned version of the model: We can see that this mask is tighter than the original mask. This was the result of fine-tuning on a small subset of images from the stamp verification dataset, and then running the tuned model on a previously unseen example. With further training and more examples, we could obtain even better results. Conclusion That's all, folks! You have now learned how to fine-tune the Segment Anything Model (SAM). If you're looking to fine-tune SAM out of the box, you might also be interested to learn that we have recently released the Segment Anything Model in Encord, allowing you to fine-tune the model without writing any code. {{SAM_CTA}}
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12 Best Supervisely Alternatives in 2024
With almost 60% of organizations planning to integrate CV into their daily operations, technology is becoming indispensable for surviving in the modern industrial space. However, reports predict users will generate around 175 zettabytes of data by 2025, calling for robust CV software solutions to streamline the development and deployment processes. Supervisely is a popular choice for managing large-scale CV operations among the many platforms available in the market. However, due to its high price tag, the tool may not be suitable for small—to medium-sized businesses. This article discusses the top alternatives to Supervisely to help you choose a platform that suits your needs. The list below highlights the major solutions mentioned in the following sections. Encord Amazon SageMaker Ground Truth VGG Image Annotator Keymakr Hive Dataloop AI Kili Computer Vision Annotation Tool (CVAT) Super.ai SuperAnnotate Segments.ai Labelbox Supervisely Supervisely is a data labeling and model-building platform that lets you annotate images, videos, point-cloud, and Digital Imaging and Communications in Medicine (DICOM) data. It also features smart labeling tools that use artificial intelligence (AI) to automate annotation workflows. The solution also helps with data management by offering data import and export functionalities with additional features to process complex datasets. Its collaboration tools allow you to share and manage assets with team members more efficiently through workspaces and projects. Further, its ecosystem includes pre-built neural networks that allow you to develop high-quality machine learning models (ML models) for multiple use cases through an intuitive user interface (UI). Users can also visualize performance metrics to analyze model failures and debug issues. Challenges While Supervisely is a cloud-based solution for data annotation and management, it has a few limitations that make it unsuitable for specific applications. Challenging to use: The tool becomes difficult to learn for new users, who may become overwhelmed by the numerous options for labeling, managing, and visualizing data. Limited annotation methods: While Supervisely supports annotation for multiple modalities, it lacks sufficient methods to label modern data objects. High up-front costs: Although Supervisely offers a free community version, it falls short of the features required for large-scale projects. The Pro and Enterprise versions have an extremely high price tag, making them only accessible to larger enterprises with hefty budgets. Due to these challenges, Supervisely is a poor option for startups in the CV industry who want a simple tool for building novel applications. So, let’s see what alternatives are available to help new businesses get started with CV systems quickly. Struggling with computer vision data management? Read our article to find out about the best data management tools. Supervisely Alternatives With Supervisely being an expensive solution, the list below mentions multiple alternatives ranked according to affordability, ease of use, annotation methods, and other data and model management features. 1. Encord Encord is an end-to-end data-centric platform that enables you to annotate, curate, and manage computer vision datasets through AI-assisted annotation features. It also provides intuitive dashboards to view insights on key metrics, such as label quality and annotator performance, to optimize workforce efficiency and ensure you build production-ready models faster. The platform consists of three tools that help you with high-quality data preparation for building AI applications: Encord Annotate: Includes basic and advanced features for labeling image data for multiple CV use cases. Encord Active: Supports active learning pipelines for debugging datasets. Index: Helps curate multi-modal data for effective management. Encord Key Features Ease-of-use: Encord Annotate offers an intuitive user interface (UI) and an SDK to label and manage annotation projects. Affordability: Encord offers multiple plans to suit different needs. It has a Starter plan for small teams, a Team plan for scaling AI models, and an Enterprise plan offering features for managing large-scale projects. Supported data types: The platform lets you annotate images, videos (and image sequences), DICOM, and mammography data. Supported annotation methods: Encord supports multiple annotation methods, including classification, bounding box, keypoint, polylines, and polygons. Other Features Scalability: The platform allows you to upload up to 500,000 images (recommended), 100 GB in size, and 5 million labels per project. You can also upload up to 200,000 frames per video (2 hours at 30 frames per second) for each project. See more guidelines for scalability in the documentation. Collaboration: To manage tasks at different stages, you can create workflows and assign roles to relevant team members. User roles include admin, team member, reviewer, and annotator. Automated labeling: The platform speeds up the annotation with automation features, including: - Segment Anything Model (SAM) to automatically create labels around distinct features in all supported file formats. - Interpolation to auto-create instance labels by estimating where labels should be created in videos and image sequences. - Object tracking to follow entities within images based on pixel information enclosed within the label boundary. Integration: Integrate popular cloud storage platforms, such as AWS, Google Cloud, Azure, and Open Telekom Cloud OSS, to import datasets. Data security: Encord complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards. It uses advanced encryption protocols to ensure data security and privacy. Best For Teams of all sizes looking for a complete CV solution to build innovative applications. Pricing Encord has a pay-per-user pricing model with Starter, Team, and Enterprise options. Having trouble curating data for computer vision? Learn about data curation for computer vision tasks 2. Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth (GT) is a labeling platform that uses human feedback to speed up annotation. It also allows you to customize, monitor, and evaluate ML models for multiple use cases. Amazon SageMaker Ground Truth Key Features Ease-of-use: The platform features an intuitive UI with clear instructions to help new users with quick learning. Annotation Methods: SageMaker GT offers all the standard labeling methods for image classification, object detection and tracking, semantic segmentation, and text classification. Other Features Reinforcement Learning with Human Feedback (RLHF): SageMaker GT allows users to enhance model training using RLHF functionality, which leverages human feedback to optimize model performance. Model Evaluation: The platform offers multiple metrics to evaluate large language models (LLMs), including response relevance, toxicity, bias, brand voice, and style. Custom Workflows: You can create customized labeling workflows through pre-defined templates for a faster annotation process. Best For Enterprises looking for a solution to build multi-modal models using text and vision. Pricing Pricing depends on the labeling workload. 3. VGG Image Annotator VGG Image Annotator (VIA) is an open-source web-based labeling tool that allows users to label large datasets through a two-stage process involving automated labeling and manual review. VIA Key Features Affordability: VIA is free to use, making it suitable for small-scale researchers who want to experiment with multiple labeling tasks. Ease-of-use: The platform is straightforward, requiring no prior expertise or complex configurations. Annotation methods: VIA supports rectangles, circles, ellipses, polygons, polylines, and key points. Other Features Supported formats: Users can import and export data in CSV and JSON formats. Filtering, Selection, and Update: VIA lets annotators filter, select, and update annotations to ensure high labeling quality. Best For Individual researchers working on small-scale projects. Pricing The tool is open-source. . 4. KeyMakr KeyMakr is a labeling service provider that helps users with image and video annotation. It also offers data curation services. Keymakr Key Features Affordability: KeyMakr is suitable for companies that find it expensive to hire and manage an in-house labeling team. Ease-of-use: Since KeyMakr handles all the labeling and technical requirements, users only need to submit their data and specify their use case. Annotation Methods: The service supports multiple methods, including bounding boxes, cuboids, polygons, semantic and instance segmentation, and key points. Other Features Data Validation: The platform offers services to validate training data to ensure its accuracy, consistency, and relevance. Data Collection and Creation: KeyMakr helps you collect and create suitable data according to your requirements. Best For Teams looking for a company to outsource their labeling projects. Pricing Pricing is not publicly available 5. Hive Hive is a content moderation platform that uses deep learning models to flag harmful or explicit content in images, videos, text, and audio. Hive Key Features Ease of use: Hive has an intuitive interface and a moderation dashboard that lets you visualize content and control model outputs through custom moderation policies. Other Features AutoML: The AutoML feature lets you build models from scratch without using any code. Embeddings: The platform lets you quickly create text embeddings to augment LLMs with retrieval augmented generation (RAG). Search: Hive offers a flexible web search functionality where users can provide an image and get relevant links to similar images. Generative AI (Gen AI): Hive features APIs to generate text, images, and videos based on textual prompts. Best For Teams looking for content moderation solutions to enhance application safety. Pricing Pricing is not publicly available. 6. Dataloop AI Dataloop is an all-in-one platform for managing data and building models that helps you through each stage of the mode development lifecycle. Dataloop Key Features Ease-of-use: The platform provides a no-code UI to build data and modeling pipelines using simple drag-and-drop functionality. Other Features Supported Data Types: Dataloop supports image, video, text, audio, and point cloud data. Active Learning Pipelines: The platform lets you create active learning pipelines to speed up annotation. Model Marketplace: Dataloop lets users pick the latest state-of-the-art (SOTA) models from its marketplace and use them directly in the platform for specific use cases. Elements: The platform enables you to add pre-built functions called elements to enhance your applications. Best For Large teams consisting of data scientists, engineers, and software developers requiring strong collaboration. Pricing Pricing is not publicly available. 7. Kili Kili is a data platform for building high-qualitytraining data to create LLMs and generative AI models. It offers relevant annotation tools and quality metrics to boost annotation speed and quality. Kili Key Features Affordability: Kili offers a free version suitable for small-scale projects with up to 100 annotations. The Grow and Enterprise versions feature custom prices based on your needs. Ease of use: The platform offers an intuitive UI for managing projects, exploring data, and analyzing annotation progress and quality. Annotation Methods: Kili supports bounding boxes, polygons, key points, cuboids, and landmark annotation. Other Features Automation: The tool lets you pre-label textual data using ChatGPT and Segment Anything Model (SAM) to label images for semantic segmentation tasks. Plugins: The platform enables you to build custom plugins through its Python SDK for more efficient labeling. Best For Teams looking for a labeling solution that supports CV and natural language processing (NLP) tasks. Pricing Kili offers custom pricing based on specific user needs. 8. CVAT The Computer Vision Annotation Tool (CVAT) is a solution for labeling images and videos in multiple formats. It uses modern deep-learning frameworks to boost annotation speed and quality. CVAT Key Features Affordability: CVAT offers a free version for personal and charges only USD 33 per month for professional and organizational use. Ease-of-use: The platform provides an easy-to-use interface with straightforward navigation and a clear workspace. Annotation Methods: The tool supports bounding boxes, polygons, polylines, points, ellipses, and cuboids. Other Features Automation: You can use built-in CV models, such as YOLO v7 and Faster RCNN, to automatically label your data. Performance Monitoring: CVAT lets you monitor annotation quality through a performance dashboard featuring metrics like objects labeled per hour, object count, timelines, etc. Best For Beginners looking for a lightweight annotation tool to create basic CV applications. Pricing CVAT offers a free, Solo, and Team version. 9. Superb AI Superb is an AI platform that offers multiple tools to curate and label data. It also helps with model training and deployment, providing apps to automate AI workflows. SuperbAI Key Features Ease-of-use: The tool features a straightforward UI with clearly organized sections highlighted with self-explanatory options. Annotation Methods: Superb Label supports bounding boxes, polygons, polylines, key points, and cuboids. Other Features Automation: The Auto-Label feature lets you annotate everyday objects using a pre-trained model that is available out of the box. Data Curation: Superb Curate generates data clusters based on embeddings to help users organize their data assets more efficiently. Superb Model: Superb Model provides a complete no-code solution to train and deploy baseline models using data curated through Superb Curate. Best For Teams looking for a solution to manage complex data. Pricing Pricing is not publicly available. 10. SuperAnnotate SuperAnnotate is an AI platform for building LLMs and Gen AI models through a user-friendly interface. It also includes annotation tools to label images, videos, text, and audio. SuperAnnotate Key Features Affordability: SuperAnnotate’s free version offers sufficient features for startups and independent researchers to work on standalone projects. Ease of use: The platform’s UI offers guided steps for building efficient LLMs and Gen AI frameworks. Annotation Methods: The tool supports bounding boxes, key points, segmentation masks, OCR annotation, speech recognition, and sound event detection. Other Features Data Curation: SuperAnnotate lets you create data subsets and query relevant items through advanced filtration and visualizations. Automation: The tool enables you to build automated pipelines to manage annotation projects at different stages of the development lifecycle. Best For Teams looking for a platform to orchestrate LLMs and Gen AI model development. Pricing Pricing is not publicly available. 11. Segments.ai Segment.ai is a data labeling tool that annotates multi-sensor data through a single interface by combining 3D point clouds and 2D image data. Segments.ai Key Features Affordability: The tool is suitable for large organizations that collect data from multiple sensors and edge devices. Ease-of-use: The platform features intuitive interfaces to label 2D and 3D image data. Annotation Methods: Segments.ai supports bounding boxes, cuboids, segmentation masks, polylines, polygons, and key points. Other Features 3D to 2D Projection: The platform allows you to project 3D cuboids on 2D bounding boxes for greater labeling accuracy. Sensor Fusion: The tool lets you combine data from multiple sensors and label them through a single interface. Moving Object Annotation: Segments.ai supports real-time interpolation and ML-based object tracking to annotate moving objects. Best For Teams looking for an advanced solution for labeling sensor data. Pricing Segments.ai offers a Team, Scale, and Enterprise version. 12. LabelBox LabelBox is a data management solution that lets you curate and label unstructured data using pre-trained foundation models to automate the annotation process. LabelBox Key Features Affordability: LabelBox offers a free version for individuals and a Starter version that charges USD 0.1 per LabelBox unit. Ease of use: The tool features user-friendly interfaces for exploring and visualizing datasets, annotating data items, and evaluating model outputs through intuitive dashboards. Annotation Methods: LabelBox supports bounding boxes, segmentation masks, polygons, polylines, and cuboids. Other Features Workflows: The tool lets you create custom pipelines to review and monitor annotation progress. Foundry: LabelBox uses Foundry, which consists of foundation models to predict labels and allows you to compare the performance of different models. Best For Medium-sized enterprises looking for a cost-effective labeling solution for CV and NLP tasks. Pricing The tool offers a Free, Starter, and Enterprise version. Supervisely Alternatives: Key Takeaways AsCV and NLP models become integrated into every aspect of business operations, investing in a scalable AI platform is becoming a strategic necessity to stay ahead of the competition. Below are a few points to remember regarding such AI platforms. Supervisely’s Limitations: While Supervisely is a comprehensive solution to manage data and build models, it is expensive and only suits organizations with extensive cloud-based infrastructure. Essential Factors: Businesses and researchers should consider a tool’s total cost of ownership, which includes its upfront costs and maintenance expenditures. Further, they must determine its ease of use and ensure it covers all the necessary features to handle the ever-evolving data volume and variety. Supervisely Alternatives: Encord, SuperAnnotate, and Kili are a few cost-effective, easy-to-use, and feature-rich alternatives to Supervisely.
April 12
8 min
Panoptic Segmentation Tools: Top 9 Tools to Explore in 2024
While image classification and object recognition remain the mainstream computer vision (CV) tasks, recent frameworks also address image segmentation methods to handle more complex scenarios. Enter panoptic segmentation: a CV task that merges the comprehensive understanding of semantic segmentation (categorizing each pixel into a class) with the precise object differentiation of instance segmentation (identifying individual object instances). Since its inception in 2017, panoptic segmentation has rapidly gained traction, as evidenced by over 200 research papers. This indicates its potential to transform how machines perceive and interact with their environments. This method is pivotal for applications requiring a detailed understanding of both 'stuff' (like sky, water, or grass) and 'things' (such as cars, animals, or people) in an image. However, the leap to panoptic segmentation introduces complex challenges, including the need for precise, pixel-level annotations, handling the sheer computational demands of processing detailed images, and developing models that can effectively learn from such rich data. This article introduces the essential considerations before adopting a panoptic segmentation tool and surveys the leading platforms in 2024. Our guide aims to assist you in selecting the most suitable solution for your vision systems, ensuring they can interpret complex environments with unprecedented clarity. We also give an overview of the top platforms, as listed below, to help you choose the best solution for the job. Encord iMerit Segments.ai Killi Technology Superb AI Mindkosh Super Annotate Hasty Labelbox Panoptic Segmentation Overview In computer vision (CV), image segmentation aims to label each pixel within an image to identify objects more accurately. The annotation method helps build computer vision models for use cases like self-driving cars, healthcare, and robotics. The technique consists of semantic, instance, and panoptic segmentation tasks. Let’s quickly discuss each in more detail. Semantic Segmentation Semantic segmentation assigns a label to each pixel within an image. It aims to detect ‘stuff’ - regions with similar patterns - and distinguish between different entities in a single image. For example, it will draw separate segmentation masks for people, cars, traffic lights, and trees in an image displaying objects on the road. What an Autonomous Vehicle Sees | Encord Annotate. Instance Segmentation Instance segmentation detects ‘things’ - countable objects - and distinguishes between each instance of the same object in an image. For example, instance segmentation will identify each person within an image as a separate entity, whereas semantic segmentation will assign the same class label to everyone in the image. Semantic (left) vs Instance Segmentation (right) Panoptic Segmentation Panoptic segmentation combines semantic and instance segmentation to produce accurate pixel-level annotations for more complex computer vision applications. It detects ‘stuff’ and ‘things’ for a richer scene understanding by merging classification and detection algorithms. Semantic vs Instance vs Panoptic Segmentation Want to learn more about Panoptic Segmentation? Here is a list of top 5 V7 Alternatives for a detailed understanding Panoptic Segmentation Challenges While panoptic segmentation is a powerful technique to improve visual understanding, it poses multiple challenges due to the following reasons: Overlapping Objects: Segmenting overlapping objects is difficult as the algorithms cannot identify object boundaries to generate accurate masks. Image Quality: Low image quality makes detecting things and classifying stuff challenging due to blur, occlusion, and unclear shapes. Lack of Training Data: Building segmentation models requires extensive, high-quality training datasets to comprehensively understand everyday objects. Developing such models from scratch is tedious and costly. Due to these issues, you must search for a suitable platform that offers pre-built segmentation frameworks and tools to efficiently label visual data of all types and formats through user-friendly interfaces. Important Factors for Segmentation Tools Investing in a segmentation platform is a strategic decision that requires careful analysis of the available solutions. However, with so many platforms flooding the market, finding the best tool for the job becomes overwhelming. So, this list below highlights the factors that will help you select the most suitable annotation tool based on your specific requirements. Annotation Methods: Multiple annotation methods, including bitmasks, polygons, bounding boxes, and key points, help you annotate and segment various data types and address complex labeling scenarios. Support for Multi-Modal Data: To ensure efficient data processing, support for images, sequences, videos, and point clouds is necessary. Scalability: Select a tool that can quickly scale up with minimal overhead. Consider its ability to manage large-scale projects and heavy workloads. Collaboration: Collaborative tools can streamline workflows by allowing teams to work on shared projects and speed up delivery. Automation: Tools with automated labeling techniques can boost annotation speed and quality. User Interface (UI): An easy-to-use interface allows you to use a platform to its full potential. Integrability: Integration with cloud storage platforms, plugins, and modeling frameworks improves functionality and lets you address domain-specific issues. Data Security: Ensure the tool complies with established international security standards to protect data privacy. Price: A labeling tool’s feature set must justify its cost by offering sufficient functionality in an affordable price range. Don’t know how to get the best image segmentation results? Read our image segmentation for computer vision best practice guide to learn more Panoptic Segmentation Tools Considering the earlier segmentation challenges, businesses must invest in a robust image annotation platform with state-of-the-art (SoTA) segmentation functionality. The list below provides an overview of the top panoptic segmentation tools ranked according to the abovementioned factors to help you with your search. 1. Encord Encord is an end-to-end, data-centric computer vision platform that improves panoptic segmentation workflows across data, labeling, and model evaluation. The platform includes three products that enable different parts of the panoptic segmentation workflow (including annotation, data management, and performance assessment). Encord Annotate: Includes basic and advanced features for labeling image and video datasets for multiple CV use cases. Index: Helps curate multi-modal data for effective management. Encord Active: Easily evaluate your segmentation model’s panoptic mask quality with task-specific metrics (like mean Panoptic Quality). Key Features Supported Annotation Methods: Encord includes a bitmask annotation and lock feature to prevent segmentation and masks from overlapping. This helps with pixel-perfect accuracy for your segmentation tasks. Supported Data Types: The platform supports images, image sequences, videos, and Digital Imaging and Communications in Medicine (DICOM). Scalability: The platform allows you to upload up to 500,000 images (recommended), 100 GB in size, and 5 million labels per project. You can also upload up to 200,000 frames per video (2 hours at 30 frames per second) for each project. See more guidelines for scalability in the documentation. Collaboration: Users can quickly collaborate with their team members through shared annotation projects that let you create custom workflows for quality assurance steps. Automation - Segment Anything Model (SAM): Starting your annotation process can be time-consuming, especially for complex images. The SAM integration offers a one-click solution to create initial annotations, speeding up the annotation process with high accuracy. User Interface: Encord lets you surgically label overlapping objects at pixel level 5x faster with enhanced zooming functionality and image loading through the Label Editor UI. Also, the Python SDK lets experienced users perform segmentation tasks programmatically. Quality Metrics: You can assess annotation performance through robust panoptic quality metrics to quickly identify areas of improvement. Integrability: You can integrate with popular cloud storage platforms such as Microsoft Azure, Google Cloud Platform (GCP), Amazon Web Services (AWS), and Open Telekom Cloud OSS to import datasets. Data Security: Encord complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards. It uses advanced encryption protocols to ensure data security and privacy. Best for Teams looking for an enterprise-grade image and video annotation solution with advanced features to produce high-quality panoptic segmentation features. Pricing Encord has apay-per-user pricing model with Starter, Team, and Enterprise options. 2. iMerit iMerit is a data labeling tool that offers Ango Hub as its primary annotation platform for images, videos, and textual data. It features auto-labeling functionality with interactive tools for detecting object boundaries. iMerit Key Features Annotation Methods: iMerit supports bounding boxes, polygons, polylines, key points, and segmentation. Users can draw polygons around objects to create segmentation masks. Supported Data Types: The platform supports images, videos, audio, textual, and DICOM data. Collaboration: iMerit lets you create shared projects and assign team members relevant roles, such as project owner, manager, annotator, and reviewer. It also allows for real-time troubleshooting, where annotators can directly notify project managers in case of issues. Automation: Plugins allow you to use pre-built models for data labeling. User Interface: The platform features an intuitive UI to create segmentation masks with holes using the polygon tool. It also features analytical reports to assess labeling performance against benchmarks for informed decision-making. Data Security: iMerit complies with the EU-U.S. Data Privacy Framework. Best For Teams looking for a labeling solution to build CV applications for manufacturing and agricultural use cases. Pricing Pricing is not publicly available. 3. Segments.ai Segments.ai is a 3D labeling platform that allows you to annotate data from multiple sensors, such as cameras, radar, and LiDAR, through a unified interface. Its sensor fusion capabilities let users view 2D and 3D data simultaneously for better context. Segments.ai Key Features Annotation Methods: The tool supports segmentation, bounding boxes, cuboids, polylines, polygons, and key points. Supported Data Types: Segments.ai supports images and 3D point-cloud data. Collaboration: Users can add multiple collaborators to a project and assign them the roles of manager, reviewer, manager, or administrator. Automation: The platform comprises advanced segmentation models that let you create segmentation masks with a single click. User Interface: Segments.ai's UI is easy to navigate, and it uses multiple drawing tools, such as polygons and brushes, to specify segmentation masks. It also features a Python SDK to help you manage data programmatically. Data Security: Segments.ai complies with the ISO 27001 standards. Best For Teams looking for a labeling solution for developing autonomous driving and robotics applications. Pricing Segments.ai offers a Team, Scale, and Enterprise version. 4. Kili Kili helps you label image and video data through batch processing and automated tools. It also offers evaluation tools to assess the performance of large language models (LLMs). Kili Key Features Annotation Methods: Kili supports bounding boxes, optical character recognition (OCR), cuboids, and semantic segmentation. It features an interactive click tool to adjust segmentation masks for different objects manually. Supported Data Types: The platform supports text, image, and video data. Collaboration: Users can add new members to labeling projects with relevant user roles. Automation: Kili allows you to use the Segment Anything Model (SAM) for high-quality segmentation and ChatGPT for pre-labeling textual data. User Interface: The platform's user-friendly interface for creating segmentation masks lets you define center points and adjust corners for more precision. Data Security: Kili is SOC 2-compliant. Best For Teams looking for a solution to create training for LLMs. Pricing Kili charges based on data usage. 5. Superb AI Superb AI is an end-to-end solution for training and deploying AI models. It offers data curation and annotation features and the ability to use machine learning (ML) models for faster labeling. SuperbAI Key Features Annotation Methods: Superb Label supports bounding boxes, polygons, polylines, and cuboids. Users can draw polygons around objects to create segmentation masks. Supported Data Types: The platform supports image, video, and point cloud data. Collaboration: The tool features project management workflows that let you assign roles to team members for different labeling tasks. Automation: The Auto-Label features enable you to select pre-built models to annotate more than 100 objects. User Interface: The UI allows you to create precise segmentation masks through the polygon tool with features to define accurate vertices. Data Security: SuperbAI complies with the SOC and ISO 27001 standards. Best for Teams looking for a solution to develop and deploy models. Pricing Pricing is not publicly available. 6. Mindkosh Mindkosh is a data labeling platform that offers AI-based annotation tools to label images, videos, and point cloud data. Its interactive segmentation functionality allows users to specify regions of interest they want to segment surgically. Mindkosh Key Features Annotation Methods: The platform supports bounding boxes, polygons, segmentation, cuboids, and key points. Supported Data Types: Mindkosh supports image, video, and point cloud data. Collaboration: Users benefit from shared workspaces and projects that let them assign labeling tasks to multiple users. Automation: The Magic Segment tool allows you to create segmentation masks automatically through a few clicks. User Interface: The interface comprises organized panels and a polygon tool to create segmentation masks. Data Security: Mindkosh uses the AWS infrastructure to host its application, making the platform compliant with all the security standards that AWS supports, including ISO 27001, SOC 1, and SOC 2. Best For Teams looking for a segmentation tool at the beginner level. Pricing Pricing is not publicly available. 7. SuperAnnotate SuperAnnotate is a data management platform that lets you create training data for CV and natural language processing (NLP) tasks. It also helps you build automated pipelines through its built-in neural networks, webhooks, and Python SDK. SuperAnnotate Key Features Annotation Methods: SuperAnnotate supports bounding boxes, key points, and segmentation. It uses SAM to create accurate segmentation maps. Supported Data Types: The tool supports image, video, text, and audio data. Collaboration: The platform allows you to create shared projects and collaborate with stakeholders for task review and distribution. Automation: Users can fine-tune base models on custom training data to automate the labeling process. User Interface: SuperAnnotate features an interactive UI with easy-to-follow options, magic select, and polygon tools for quick segmentation. Data Security: SuperAnnotate complies with SOC 2, HIPAA, GDPR, and ISO 27001 standards. Best For Teams looking for a solution that helps them implement MLOps pipelines. Pricing Pricing is not publicly available. 8. Hasty Hasty is a lightweight annotation tool that uses AI models to label your data and manage quality assurance workflows. It features a model playground that lets you experiment with state-of-the-art deep-learning models to compare labeling output using different configurations. Hasty Key Features Annotation Methods: The tool supports object detection, image classification, and semantic and instance segmentation methods. Supported Data Types: Hasty supports image and video data. Scalability: The platform’s active learning pipelines make it suitable for labeling extensive datasets. Automation: Hasty features AI-assisted labeling and automated consensus scoring for faster annotation and error resolution. User Interface: It offers a user-friendly interface for creating models to annotate data. Data Security: Hasty complies with the ISO 27001 standards. Best For Teams looking for a quick solution to label small-scale image datasets. Pricing Pricing is not publicly available. 9. Labelbox Labelbox is a data curation, annotation, and model evaluation platform. It features SoTA foundation models, reinforcement learning with human feedback (RLHF) functionality, and analytical reports to assess labeling quality. LabelBox Key Features Annotation Methods: Labelbox supports bounding boxes, cuboids, polygons, polylines, key points, and segmentation masks. Supported Data Types: The platform supports images, videos, text, and audio data. Collaboration: Labelbox lets you create project-based groups with team members having specialized roles according to their expertise. Automation: The AutoSegment tool lets you create masks for individual objects to perform instance segmentation tasks. User Interface: The platform features an easy-to-navigate, no-code interface for labeling data and creating segmentation masks. Data Security: Labelbox complies with the GDPR, ISO 27001, SOC2, HIPAA, CCPA, DSS, NIST, and U.S. Government standards. Best For Teams looking for a data management solution that integrates with the latest SOTA CV and LLM models. Pricing The tool offers a Free, Starter, and Enterprise version. Panoptic Segmentation Tools: Key Takeaways As the field of computer vision expands to solve real-world problems, data annotation becomes challenging due to the rising volume and variety of data. The trend calls for robust annotation and segmentation platforms to help organizations of all sizes efficiently manage labeling processes for extensive datasets with minimal overhead. Below are some of the key points to remember regarding segmentation tools. Segmentation: Building segmentation modes from scratch is challenging due to poor data quality and lack of training data. Users need efficient tools to make the segmentation task easier. Factors to Consider: Advanced panoptic, instance, and semantic segmentation features. Support for multi-modal data and collaborative tools is essential when investing in a segmentation platform. Top Panoptic Segmentation Tools: Encord, iMerit, and Segments.ai are popular solutions offering automated segmentation functionality with robust collaborative features.
April 10
8 min
Setting Up a Computer Vision Testing Platform
When machine learning (ML) models, especially computer vision (CV) models, move from prototyping to real-world application, they face challenges that can hinder their performance and reliability. Gartner's research reveals a telling statistic: just over half of AI projects make it past the prototype stage into production. This underlines a critical bottleneck—the need for rigorous testing. Why do so many models fail to make it to real-world applications? At Encord, ML teams tell us that model performance bottlenecks include: the complexity of ML models and diverse datasets, the need for testing processes that can handle large amounts of data, the need for automation to handle repetitive tasks, and the need for teams to collaborate to improve ML systems. This article will teach you the intricacies of setting up a computer vision (CV) testing platform. You will gain insights into the essence of thorough test coverage—vital for the unpredictable nature of CV projects—and learn about managing test cases effectively. You will also learn how collaborative features can be the centerpiece of successful testing and validation. By the end of the article, you should understand what it takes to set up a CV testing platform. Challenges Faced by Computer Vision Models in Production Computer Vision (CV) models in dynamic production environments frequently encounter data that deviates significantly from their training sets—be it through noise, missing values, outliers, seasonal changes, or general unpredictable patterns. These deviations can introduce challenges that compromise model performance and reliability. Building reliable, production-ready models comes with its own set of challenges. In this section, you will learn why ensuring the reliability of CV models is a complex task. We are going to look at the following factors: Model Complexity: The intricate architecture of CV models can be challenging to tune and optimize for diverse real-world scenarios. Hidden Stratification: Variations within classes the model hasn't explicitly trained on can lead to inaccurate predictions. Overfitting: A model might perform exceptionally well on the training data but fail to generalize to new, unseen data. Model Drift: Changes in real-world data over time can gradually decrease a model's accuracy and applicability. Adversarial Attacks: Deliberate attempts to fool models using input data crafted to cause incorrect outputs. Understanding these challenges is the first step toward building robust, production-ready CV models. Next, we will explore strategies to mitigate these challenges, ensuring your models can withstand the rigors of real-world application. 🚀 Model Complexity As CV models, particularly visual foundation models (VFMs), visual language models (VLMs), and multimodal AI models, grow in complexity, they often become 'black boxes.' This term refers to the difficulty in understanding how these models make decisions despite their high accuracy. Because these models have complicated, multi-layered architectures with millions of parameters, it is hard to figure out the reasoning behind their outputs. Confidence in the model's performance can be challenging, mainly when it produces unexpected predictions. Consider a security surveillance system with advanced visual recognition to spot suspicious activity. This system, powered by a complex visual language model (VLM), is trained on lots of video data encompassing various scenarios from numerous locations and times. The system can accurately identify threats like unattended bags in public spaces and unusual behavior, but its decision-making process is unclear. Security personnel may struggle to understand why the system flags a person or object as suspicious. The model may highlight factors like an object's size, shape, or movement patterns, but it is unclear how these factors are synthesized to determine a threat. This opacity raises concerns about the model's trustworthiness and the potential for false positives or negatives. The lack of interpretability in such CV models is not just an academic issue but has significant real-world consequences. It affects the confidence of those relying on the system for public safety, potentially leading to mistrust or misinterpretation of the alerts generated. Want to dig deeper into these models? Watch our webinar, ‘Vision Language Models: Powering the Next Chapter in AI.’ Hidden Stratification Precision, accuracy, recall, and mean Average Precision (mAP) are commonly used metrics when evaluating the performance of CV models. However, it's important to remember that these metrics may not provide a complete picture of the model's performance. A model could be very accurate when trained on a specific dataset, but if that dataset doesn't represent the real-world scenario, the model may perform poorly. This dilemma is called hidden stratification. Hidden stratification occurs when the training data doesn't have enough representative examples of certain groups or subgroups. For instance, a model trained on a dataset of images of primarily Caucasian patients may struggle to accurately diagnose skin cancer in black patients. This could raise serious inclusivity concerns, especially in mission-critical applications. See Also: The ultimate guide to data curation in computer vision. Overfitting A model could learn so well from the training data that it cannot make correct predictions on new data, which could lead to wrong predictions on real-world data in production systems. You have probably encountered this before: You train a model to classify images of cats and dogs with a 1000-image dataset split evenly between the two classes and trained for 100 epochs. The model achieves a high accuracy of 99% on the training data but only manages 70% accuracy on a separate test dataset. The discrepancy suggests overfitting, as the model has memorized specific details from the training images, like ear shape or fur texture, rather than learning general features that apply to all cats and dogs. Model Drift You consider a model “drifting” when its predictive accuracy reduces over time when deployed to production. If you do not build your ML system so that the model can adapt to real-world data changes, it might experience sudden drifts or slow decay over time, depending on how your business patterns change. One practical example is to consider an autonomous vehicle's pedestrian detection system. Initially trained on extensive datasets covering various scenarios, such a system might still experience model drift due to unforeseen conditions, like new types of urban development or changes in pedestrian behavior over time. For instance, introducing electric scooters and their widespread use on sidewalks presents new challenges not in the original training data, potentially reducing the system's accuracy in identifying pedestrians. Recommended Read: Best Practices to Improve ML Model Performance and Mitigate Model Drfit. Adversarial Attacks Adversarial attacks consist of deliberately crafted inputs that fool models into making incorrect predictions. These attacks threaten ML applications, from large language models (LLMs) to CV systems. While prompt injection is a known method affecting text-based models, CV models face similar vulnerabilities through manipulated images (image perturbation) or objects within their field of view. A notable demonstration of this was by researchers at the University of California, Berkeley, in 2016. They executed an adversarial attack against a self-driving car system using a simple sticker, misleading the car's vision system into misidentifying the type of vehicle ahead. This manipulation caused the self-driving car to stop unnecessarily, revealing how seemingly innocuous input data changes can impact decision-making in CV applications. Adversarial attacks are challenging because of their subtlety and precision. Only minor alterations are often needed to deceive an AI system, making detection and prevention particularly challenging. This underscores the critical importance of rigorously testing ML models to identify and mitigate such vulnerabilities. You can make CV systems more resistant to these attacks by testing them thoroughly and using adversarial simulation as part of your process for reliable applications. Testing Computer Vision Models and Applications Testing CV applications is more complex than testing traditional software applications. This is because the tests only partially depend on the software. Instead, they rely on factors such as the underlying business problem, dataset characteristics, and the models you trained or fine-tuned. Therefore, establishing a standard for testing CV applications can be complex. Understanding the Computer Vision Testing Platform A CV test platform forms the backbone of a reliable testing strategy. It comprises an ecosystem of tools and processes that facilitate rigorous and efficient model evaluation. The platform can help teams automate the testing process, monitor test results over time, and rectify issues with their models. Essential components of a robust CV testing platform include: Test Data Management: Involves managing the test data (including versioning and tracing lineage) to mirror real-world scenarios critical for models to understand such conditions. With this component, you can manage the groups and sub-groups (collections) to test your model against before to ensure production readiness. Test Reporting: An effective reporting system (dashboards, explorers, visualizations, etc.) is instrumental in communicating test outcomes to stakeholders, providing transparency, and helping to track performance over time. Model Monitoring: The platform should also include a component that monitors the model's performance in production, compares it against training performance, and identifies any problems. The monitoring component can track data quality, model metrics, and detect model vulnerabilities to improve the model’s robustness against adversarial attacks. Test Automation: Setting up automated testing as part of a continuous integration, delivery, and testing (CI/CD/CT) pipeline allows you to configure how you validate the model behavior. This ensures that models work as expected by using consistent and repeatable tests. Recommended Read: New to model monitoring? Check out our guide to ML model observability. Setting Up Your Computer Vision Testing Platform Having established what the CV testing platform is and its importance, this section will describe what a good platform setup should look like. 1. Define Test Cases In ML, test cases are a set of conditions used to evaluate an ML model's performance in varying scenarios and ensure it functions as expected. Defining robust model test cases is crucial for assessing model performance and identifying areas to improve the model’s predictive abilities. For instance, you trained a model on diverse driving video datasets and parking lot videos. You then used it on a dashcam system to count the number of vehicles while driving and in a parking lot. The successfully trained model performs admirably in Boston with cameras installed on various dashcams and across parking lots. An example of the Berkley Diverse Driving Dataset in Encord Active. Stakeholders are satisfied with the proof-of-concept and are asking to scale the model to include additional cities. Upon deploying the model in a new area in Boston and another town, maybe Minnesota, new scenarios emerge that you did not consider. In one parking garage in Boston, camera images are slightly blurred, contrast levels differ, and vehicles are closer to the cameras. In Minnesota, snow is on the ground, the curbside is different, various lines are painted on the parking lot, and new out-of-distribution car models (not in the training data) are present. Production scenario for the CV model in a Minnesota snowy parking lot (left) and Boston parking house in a dashcam (right). These scenarios are strange to the model and will harm its performance. That is why you should consider them test cases when testing or validating the model's generalizability. Defining the test cases should begin with preparing a test case design. A test case design is the process of planning and creating test cases to verify that a model meets its requirements and expected behavior. It involves identifying what aspects of the ML model need to be tested and how to test them. Recommended Read: Model Test Cases: A Practical Approach to Evaluating ML Models. Steps in test case design Define test objectives: Clearly state what the tests are expected to achieve. This starts with identifying failure scenarios, which may include a wide range of factors, such as changing lighting conditions, vehicle types, unique perspectives, or environmental variations, that could impact the model's performance. For example, in a car parking management system, some of the potential edge cases and outliers could include snow on the parking lot, different types of lines painted on the parking lot, new kinds of cars that weren't in the training data, other lighting conditions at varying times of day, different camera angles, perspectives, or distances to cars, and different weather conditions, such as rain or fog. By identifying scenarios where the model might fail, you can develop test cases that evaluate the model's ability to handle these scenarios effectively. After defining the test objectives, the next step is selecting test data for each case. See Also: How to Analyze Failure Modes of Object Detection Models for Debugging. Select test data and specify test inputs: When selecting input data, consider a diverse range of scenarios and conditions. This ensures that the data is representative of the defined test cases, providing a comprehensive understanding of the system or process being analyzed. Be sure to include edge cases in your selection, as they can reveal potential issues or limitations that may not be apparent with only typical data. In the car parking management system above, obtain samples of video images from different locations and parking lot types. Determine expected ML model outcomes and behaviors: Specify each test case's expected results or behaviors. This includes defining what the model should predict or what the software should do in response to specific inputs. Based on the failure mode scenarios of the model in the car parking management system, here are some recommendations: The model should achieve a mean Average Precision (mAP) of at least 0.75 for car detection when cars are partially covered or surrounded by snow and in poorly lit parking garages. The model's accuracy should be at least 98% for partially snow-covered parking lines. Create test cases: Document each test case with inputs, actions, and expected outcomes for clear and effective evaluation. Execute test cases: Execute the prepared test cases systematically to evaluate the ML model. Where possible, utilize automated testing to ensure efficiency and consistency. Record the actual outcomes to facilitate a detailed comparison with the expected results. Analyzing results: Review the outcomes using established metrics such as precision, recall, and f1-score. Document any deviations and conduct a thorough analysis to uncover the root cause of each discrepancy. Common issues may include model overfitting, data bias, or inadequate training. Useful Read: 5 Ways to Reduce Bias in Computer Vision Datasets. Iterative improvement: Upon identifying any issues, take corrective actions such as adjusting the model's hyperparameters, enriching the dataset with more samples and subsets, or refining the features. After modifications, re-run the test cases to verify improvements. This iterative process is essential for achieving the desired model performance and reliability. Keep iterating through this process until the model's performance aligns with the objectives defined in your test cases. 2. Compute Environment Most CV tests involving complex models and large datasets are computationally intensive. Adequate computing resources are essential for efficient and effective testing. Without these resources, you may encounter scalability issues, an inability to manage large visual test datasets, longer testing times, crashing sessions, insufficient test coverage, and a higher risk of errors. Strategies for ensuring adequate compute resources for CV testing: Resource estimation: Begin assessing the computational load by considering the model's size and complexity, dataset volume, and the number of tests. This will help in estimating the required resources to ensure tests run smoothly. Using cloud computing: Use services from cloud providers such as AWS, Azure, or GCP. These platforms provide scalable resources to accommodate varying workloads and requirements. Tools like Encord Active—a comprehensive CV testing and evaluation platform—streamline the process by connecting to cloud storage services (e.g., AWS S3, Google Cloud Storage, Azure Blob Storage) to retrieve test data. Distributed computing: Use distributed computing frameworks like Apache Spark to distribute CV tests across multiple machines. This can help reduce the time it takes to execute the tests. Optimization of tests: Optimize your CV tests by choosing efficient algorithms and data structures to minimize the computational resources required. ML teams can ensure their models are fully tested and ready for production by carefully planning how to use modern cloud-based solutions and distributed computing. 3. Running Tests and Analyzing Results For a smooth CV testing process, follow these comprehensive steps: Data and code preparation: Transfer the test data and code to the computing environment using secure file transfer methods or uploading directly to a cloud storage service. Install dependencies: Install the CV testing framework or tool you have chosen to work with and any additional libraries or tools required for your specific testing scenario. Configure the test environment: Set the necessary environment variables and configuration parameters. For example, define database connection strings, store secrets, or specify the path to the dataset and model artifacts. Execute tests: Run the tests manually or through an automation framework. Encord Active, for instance, can facilitate test automation by computing quality metrics for models based on the predictions and test data. Collect and analyze results: Gather the test outputs and logs, then analyze them to evaluate the model's performance. This includes mAP, Mean Square Error (MSE), and other metrics relevant to the use case and model performance. 4. Automating ML Testing with Continuous Integration, Delivery, and Testing (CI/CD/CT) Continuous integration, delivery (or deployment), and testing for CV automates the process of building, testing, and deploying the models. This automation is crucial in ensuring that models are reliable and issues are identified and resolved early on. Steps for a robust CI/CD/CT pipeline in ML: Pipeline trigger: Automate the pipeline to trigger upon events like code commits or set it for manual initiation when necessary. Code repository cloning: The pipeline should clone the latest version of the codebase into the test environment, ensuring that tests run on the most current iteration. Dependency installation: The pipeline must automatically install dependencies specific to the model, such as data processing libraries and frameworks. Model training and validation: In addition to training, the pipeline should validate the ML model using a separate dataset to prevent overfitting and ensure that the model generalizes well. Model testing: Implement automated tests to evaluate the model's performance on out-of-distribution, unseen data, focusing on the model metrics. Model deployment: The pipeline could automatically ship the model upon successful testing. Depending on the pipeline configuration, this may involve a soft deployment to a staging environment or a full deployment to production. Platforms like GitHub Actions, CircleCI, Jenkins, and Kubeflow offer features that cater to the iterative nature of ML workflows, such as experiment tracking, model versioning, and advanced deployment strategies. Advantages of CI/CD/CT for computer vision Enhanced model quality: Rigorous testing within CI/CT pipelines contributes to high-quality, reliable models in production environments. Reduced error risk: Automation minimizes human error, especially during repetitive tasks like testing and deployment. Efficiency in development: Automating the build-test-deploy cycle accelerates development and enables rapid iteration. Cost-effectiveness: The practices reduce resource waste, translating to lower development costs. Best practices By incorporating best practices and being mindful of common pitfalls, you can make your pipeline robust and effective. These practices include: Ensure your pipeline includes: Data and model versioning to track changes over time. Comprehensive test suites that mirror real-world data and scenarios. Regular updates to the test suite reflect new insights and data. Pitfalls to avoid: Avoid underestimating the complexity of models within the CI pipeline. Prevent data leakage between training and validation datasets. Ensure that the CI pipeline is equipped to handle large datasets efficiently. Throughout this article, you have explored the entire workflow for setting up a testing platform. You might have to configure and maintain several different components. Setting these up might require cross-functional and collaborative development and management efforts. So, most teams we have worked with often prefer using a platform incorporating all these features into one-click or one-deploy configurations. No spinning up servers, using tools that are not interoperable, or maintaining various components. Enter CV testing platforms! Using Platforms for Testing Computer Vision Models Over Building One Various platforms offer tools for testing ML models. Some examples are Encord Active, Kolena, Robust Intelligence, and Etiq.ai. Encord Active, for instance, excels at debugging CV models using data-centric quality metrics to uncover hidden model behaviors. It provides a suite of features for organizing test data, creating Collections to analyze model performance on specific data segments, and equipping teams to devise comprehensive tests. With Active Cloud, you can manage test cases and automatically compute metrics for your models through a web-based platform or the Python client SDK (to import model predictions). Conclusion: Using A Robust Testing Platform Throughout this article, you have learned that a robust testing platform is vital to developing reliable and highly-performant computer vision models. A well-set-up testing platform ensures comprehensive test coverage, which is crucial for verifying model behavior under diverse and challenging conditions. Managing your test cases and seamless team collaboration is also essential for addressing issues like hidden stratification—where models perform well on average but poorly on subgroups or slices—overfitting, and model drift over time. Remember to document the process and results of your accountability tests to inform future testing cycles. Regularly reviewing and refining your test strategy is key to maintaining an effective model development lifecycle. With the continuous advancements in traditional and foundation ML models over the next few years, we expect the integration of robust testing platforms to become increasingly critical. They will be pivotal in driving the success of LLM and ML applications, ensuring they deliver ongoing value in real-world scenarios. Your ML team's goal should be clear: to enable the development of CV models that are not only high-performing but also resilient and adaptable to the ever-changing data landscape they encounter.
April 9
8 min
Encord Monthly Wrap: March Industry Newsletter
Hi there, Welcome to the Computer Vision Monthly Wrap for March 2024! Here’s what you should expect: 🍏 MM1 - Methods, analysis, and insights from multimodal LLM pre-training by researchers at Apple. 📸 HyperLLaVA for developing adaptable and efficient AI systems that can excel across various multimodal tasks. 📽️ Understanding Mora, an open-source alternative to OpenAI’s text-to-video model. ⚒️ Developer resources to use for your next vision AI application. ☁️ Top 15 image segmentation repos for your next segmentation applications. 🤖 Google’s Video Gaming Companion: Scalable Instructable Multiworld Agent [SIMA]. Let’s dive in! Top Picks for Computer Vision Papers This Month MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training This paper from Apple researchers is an in-depth analysis of multimodal large language model (MLLM) pre-training. They focused on developing efficient models by exploring architectural components and data selection strategies. The study shows how integrating different kinds of data—such as text-only data, interleaved image-text, and image-caption pairs—can improve few-shot learning performance on a range of benchmarks. It is a big step forward for AI's ability to understand and process complex multimodal inputs. What’s impressive? 🤯 The researchers scaled the model using Mixture of Experts (MoE) and dense model variants, which shows its complex architecture and how it can improve performance by smartly distributing computing resources. This is crucial for ensuring the model can work well in many real-world applications. The model's superior few-shot learning performance across several benchmarks indicates impressive improvements in how AI learns from limited data and interleaved data, which could help us build agile and adaptable AI systems. The 30B (billion) parameter-dense model beats prior state-of-the-art (SOTA) on VQA (Visual Question Answering) dataset and captioning tasks. How can you apply it? ⚒️ If you are conducting multimodal AI research, consider applying insights from MM1's architectural decisions, training recipes, and data strategies to improve how you develop new AI models. You can use the model for creative tasks like generating and curating context-aware content across different media. This will make it easier for people to create interesting and useful content. If you are building recommendation engines, use them to analyze user preferences across different media types for more personalized content suggestions. 📜 Read the paper on Arxiv. If that’s a lot, we also put out an explainer that helps you quickly get to the important bits. It provides a walkthrough on how to use the open-source YOLOv9 release to create custom datasets. HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models Advancements in Multimodal Large Language Models (MLLMs) have shown that scaling them up improves their performance on downstream multimodal tasks. But the current static tuning strategy may constrain their performance across different tasks. This paper discusses HyperLLaVA, a framework that circumvents the problems with static tuning methods by letting visual and language experts dynamically tune both the projector (which turns visual data into a format that language models can understand) and the LLM parameters. What’s impressive? 👀 It uses a unique training methodology that first aligns visual-language features and then refines language model tuning with multimodal instructions, optimizing the model’s comprehension and responsiveness. It shows amazing progress in MLLM benchmarks (MME, MMBench, SEED-Bench, and LLaVA-Bench), which opens the door for AI systems that are more nuanced, adaptable, and capable of handling complex multimodal data. Unlike static models, HyperLLaVA uses HyperNetworks to adaptively generate parameters for projectors and LLMs based on input, which helps with task-specific optimizations. 📜 Read the paper on Arxiv. Google’s Video Gaming Companion: Scalable Instructable Multiworld Agent [SIMA] How do you train an AI agent to be a generalist? Google DeepMind’s latest AI agent, SIMA, short for Scalable Instructable Multiworld Agent, helps us understand precisely how. SIMA interacts with the environment in real-time using a generic human-like interface. It receives image observations and language instructions as inputs and generates keyboard and mouse actions as outputs. SIMA is trained on a dataset of video games, including Satisfactory, No Man's Sky, Goat Simulator 3, and Valheim. Here is an explainer post that distills the technical paper with the most important bits you need to know. MORA: The Advanced Multi-Agent Video Generation Framework Mora is a multi-agent framework designed for generalist video generation. Based on OpenAI's Sora, it aims to replicate and expand the range of generalist video generation tasks. It distinguishes itself from Sora by integrating several visual AI agents into a cohesive system. Here are the video generation tasks it can do: 1️⃣ Text ➡️ Video 2️⃣ Text + Image ➡️ Video 3️⃣ Extending Videos 📈 4️⃣ Text + Video ➡️ Video 5️⃣ Video merging 🤝 6️⃣ Simulating digital worlds 🤖 Here is an explainer post that distills the technical paper with the most important bits you need to know. Developer Resources You’d Find Useful Gemini 1.5 Pro API Support in AI Studio for Developers → Google started rolling out Gemini 1.5 Pro support for developers! This means you can start developing AI apps with Gemini 1.5 Pro, which comes with a standard 128,000 token context window, and you can build with the 1M token context window! 15 Interesting GitHub Repositories for Image Segmentation → If you are building an application involving image segmentation, this article includes 15 GitHub repositories that showcase different approaches to segmenting complex images. The Generative AI In-Vehicle Experience Powered by NVIDIA DRIVE → In a recent video, NVIDIA unveiled a new in-vehicle AI experience powered by NVIDIA DRIVE. This multimodal AI assistant can perceive, reason with, and assist drivers with features like surround visualization, access to a knowledge base, and the ability to read and understand text. This new experience will likely help with developing more context-aware autonomous vehicle systems. Here are other quick finds if you 💓Encord and computer vision data stuff ⚡: Join the Encord Community to discuss this newsletter. Data-centric computer vision blog. Till next month, have a super-sparkly time!
April 5
8 min
Fine-Tuning VLM: Enhancing Geo-Spatial Embeddings
As the world generates an ever-expanding volume of visual content, the need for efficient data curation becomes increasingly important. Whether it’s satellite imagery, aerial photographs, or remote sensing data, organizing and annotating these visuals is essential for scientific research, urban planning, disaster response, and more. In this blog post, we explore how fine-tuning the Contrastive Language-Image Pre-Training or CLIP model with the RSICD dataset—a collection of remote sensing images and captions—revolutionizes how we curate geospatial data. Unlike traditional image processing methods, CLIP offers advanced capabilities like semantic search and multilingual annotations, improving the processing and analysis of geospatial information. Fine-Tuning Vision-Language Models (VLMs) Fine-tuning Vision-Language Models (VLM) to enhance embeddings is a cutting-edge approach to data curation. VLMs are advanced models that combine visual and textual understanding, making them incredibly powerful tools for processing and analyzing multimedia data. By fine-tuning these models specifically for geospatial tasks, we aim to improve the accuracy and efficiency of location-based data processing and analysis. Geo-spatial Embeddings Geo-spatial embeddings refer to representations of geographical locations in a continuous vector space, where each location is encoded as a vector with semantic meaning. These embeddings are crucial for various applications such as geographical information systems (GIS), location-based recommendation systems, urban planning, environmental monitoring, and disaster response, among others. However, generating accurate geospatial embeddings from heterogeneous data sources poses significant challenges due to the complexity and diversity of spatial information. At Encord, we address these challenges by fine-tuning VLMs like CLIP to produce more accurate and semantically rich geospatial embeddings. This can help streamline your data curation process with new possibilities in how you use geospatial data. Importance of Fine-Tuning VLM in Data Curation The importance of fine-tuning VLMs in data curation can be understood through several key aspects: Semantic Understanding VLMs are capable of understanding and interpreting both visual and textual information simultaneously. By fine-tuning these models on specific datasets relevant to a particular domain, such as medical imaging or satellite imagery, they can learn to associate visual features with corresponding textual descriptions. This semantic understanding greatly enriches the curated data by providing context and meaning to the information being processed. So the annotators can quickly identify and tag images based on textual descriptions, improving dataset organization and curation. Adaptability to Domain-Specific Requirements Different domains have unique data characteristics and requirements. Fine-tuning VLMs allows for customization and adaptation to these domain-specific needs. For example, here we are fine-tuning the VLM model to improve geospatial embeddings. Improved Data Accuracy Fine-tuning VLMs enables them to better capture the complexities of the data being curated. This results in improved relevance and accuracy of the curated datasets as the models learn to extract and highlight the most relevant features and information. Consequently, curated datasets become more valuable for downstream tasks such as machine learning, analytics, and decision-making processes. Fine-Tuning CLIP with RSICD CLIP Contrastive Language-Image Pre-training or CLIP, developed by OpenAI, is a powerful multimodal model that bridges the gap between natural language and visual content. It learns to associate images and their corresponding captions in a self-supervised manner, enabling it to perform tasks like image search, zero-shot classification, and more. RSICD Dataset The Remote Sensing Image Caption Dataset or RSICD serves as our training ground. Comprising approximately 10,000 satellite images, this dataset features both image labels and descriptive captions. These captions provide valuable context, making RSICD an ideal candidate for fine-tuning CLIP. Why Fine-Tune CLIP with RSICD? Geo-Spatial Specificity Satellite images differ significantly from everyday photos. Captured by orbiting satellites, differ from normal ground-level images in scale, perspective, and resolution. By fine-tuning CLIP with RSICD, we tailor the model to understand the complexities of geospatial data. This specificity enhances its ability to handle satellite imagery effectively. Strengthen Search Ability By incorporating captions during fine-tuning, we ensure that the model embeds both image and text information cohesively. Consequently, CLIP becomes adept at natural language search and image retrieval. Embedding Space Before Fine-Tuning. The scattered arrangement of clusters represents data points in the initial embedding space. Embedding Space After Fine-Tuning. A more refined and cohesive grouping of data points indicates an improved embedding space post-fine-tuning. Zero-Shot Performance Evaluation We evaluate the model’s zero-shot performance using ground truth labels. This involves assessing whether the textual embeddings align with the image embeddings. Such alignment validates the consistency of CLIP’s image-text capabilities. Significance of Fine-Tuning CLIP with RSICD Geo-Spatial Annotation Precision Contextual Understanding: RSICD provides satellite images alongside descriptive captions. By fine-tuning CLIP, we enhance its ability to understand the nuances of geospatial features—mountains, rivers, forests, urban areas, and more. Accurate Labeling: Curators can annotate images with greater precision. Whether identifying specific land cover types or pinpointing landmarks, CLIP ensures context-aware annotations. Efficient Data Exploration Semantic Search: Curators and researchers can query the dataset using natural language. CLIP retrieves relevant images based on textual descriptions. For instance, searching for “coastal erosion” yields coastal satellite imagery. Time Savings: Manual exploration of thousands of images becomes streamlined. CLIP acts as a smart filter, presenting relevant visuals promptly. Consistent Labeling and Quality Control Alignment of Embeddings: During fine-tuning, CLIP learns to align image embeddings with textual embeddings. Curators can cross-check whether the textual descriptions match the visual content. Uniform Annotations: Consistent labeling improves model training and downstream tasks. Whether detecting deforestation or urban sprawl, CLIP ensures uniformity. In summary, fine-tuning CLIP with RSICD empowers data curators by providing efficient search, consistent labeling, multilingual support, and domain-specific expertise. As we embrace this powerful tool, we pave the way for smarter, more accessible datasets.
April 4
5 min
YOLO Object Detection Explained: Evolution, Algorithm, and Applications
What is YOLO Object Detection? YOLO (You Only Look Once) models are real-time object detection systems that identify and classify objects in a single pass of the image. What is Object Detection? Object detection is a critical capability of computer vision that identifies and locates objects within an image or video. Unlike image classification, object detection not only classifies the objects in an image, but also identifies their location within the image by drawing a bounding box around each object. Object detection models, such as R-CNN, Fast R-CNN, Faster R-CNN, and YOLO, use convolutional neural networks (CNNs) to classify the objects and regressor networks to accurately predict the bounding box coordinates for each detected object. Image Classification Image classification is a fundamental task in computer vision. Given an input image, the goal of an image classification model is to assign it to one of a pre-defined set of classes. Most image classification models use CNNs, which are specifically designed to process pixel data and can capture spatial features. Image classification models are trained on large datasets (like ImageNet) and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. Object Localization Object localization is another important task in computer vision that identifies the location of an object in the image. It extends the image classification model by adding a regression head to predict the bounding box coordinates of the object. The bounding box is typically represented by four coordinates that define its position and size. Object localization is a key step in object detection, where the goal is not just to classify the primary object of interest in the image, but also to identify its location. Classification of Object Detection Algorithms Object detection algorithms can be broadly classified into two categories: single-shot detectors and two-shot(or multi-shot) detectors. These two types of algorithms have different approaches to the task of object detection. Single-Shot Object Detection Single-shot detectors (SSDs) are a type of object detection algorithm that predict the bounding box and the class of the object in one single shot. This means that in a single forward pass of the network, the presence of an object and the bounding box are predicted simultaneously. This makes SSDs very fast and efficient, suitable for tasks that require real-time detection. Structure of SSD Examples of single-shot object detection algorithms include YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector). YOLO divides the input image into a grid and for each grid cell, predicts a certain number of bounding boxes and class probabilities. SSD, on the other hand, predicts bounding boxes and class probabilities at multiple scales in different feature maps. Two-Shot Object Detection Two-shot or multi-shot object detection algorithms, on the other hand, use a two-step process for detecting objects. The first step involves proposing a series of bounding boxes that could potentially contain an object. This is often done using a method called region proposal. The second step involves running these proposed regions through a convolutional neural network to classify the object classes within the box. Examples of two-shot object detection algorithms include R-CNN (Regions with CNN features), Fast R-CNN, and Faster R-CNN. These algorithms use region proposal networks (RPNs) to propose potential bounding boxes and then use CNNs to classify the proposed regions. Both single-shot and two-shot detectors have their strengths and weaknesses. Single-shot detectors are generally faster and more efficient, making them suitable for real-time object detection tasks. Two-shot detectors, while slower and more computationally intensive, tend to be more accurate, as they can afford to spend more computational resources on each potential object. Object Detection Methods Object Detection: Non-Neural Methods Viola-Jones object detection method based on Haar features The Viola-Jones method, introduced by Paul Viola and Michael Jones, is a machine learning model for object detection. It uses a cascade of classifiers, selecting features from Haar-like feature sets. The algorithm has four stages: Haar Feature Selection Creating an Integral Image Adaboost Training Cascading Classifiers Despite its simplicity and speed, it can achieve high detection rates. Scale-Invariant Feature Transform (SIFT) SIFT is a method for extracting distinctive invariant features from images. These features are invariant to image scale and rotation, and are robust to changes in viewpoint, noise, and illumination. SIFT features are used to match different views of an object or scene. Histogram of Oriented Gradients (HOG) HOG is a feature descriptor used for object detection in computer vision. It involves counting the occurrences of gradient orientation in localized portions of an image. This method is similar to edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells. Object Detection: Neural Methods Region-Based Convolutional Neural Networks (R-CNN) Region-Based CNN uses convolutional neural networks to classify image regions in order to detect objects. It involves training a CNN on a large labeled dataset and then using the trained network to detect objects in new images. Region-Based CNN and its successors, Fast R-CNN and Faster R-CNN, are known for their accuracy but can be computationally intensive. Faster R-CNN Faster R-CNN is an advanced version of R-CNN that introduces a Region Proposal Network (RPN) for generating region proposals. The RPN shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. The RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. Faster R-CNN is faster than the original R-CNN and Fast R-CNN because it doesn’t need to run a separate region proposal method on the image, which can be slow. Mask R-CNN Mask R-CNN extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. This allows Mask R-CNN to generate precise segmentation masks for each detected object, in addition to the class label and bounding box. The mask branch is a small fully convolutional network applied to each RoI, predicting a binary mask for each RoI. Mask R-CNN is simple to train and adds only a small computational overhead, enabling a fast system and rapid experimentation. Single Shot Detector (SSD) SSD is a method for object detection that eliminates the need for multiple network passes for multiple scales. It discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. SSD is faster than methods like R-CNN because it eliminates bounding box proposals and pooling layers. RetinaNet RetinaNet uses a feature pyramid network on top of a backbone to detect objects at different scales and aspect ratios. It introduces a new loss, the Focal Loss, to deal with the foreground-background class imbalance problem. RetinaNet is designed to handle dense and small objects. EfficientDet EfficientDet is a method that scales all dimensions of the network width, depth, and resolution with a compound scaling method to achieve better performance. It introduces a new architecture, called BiFPN, which allows easy and efficient multi-scale feature fusion, and a new scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. EfficientDet achieves state-of-the-art accuracy with fewer parameters and less computation compared to previous detectors. You Only Look Once (YOLO) YOLO, developed by Joseph Redmon et al., frames object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. It looks at the whole image at test time so its predictions are informed by global context in the image. YOLO is known for its speed, making it suitable for real-time applications. You Only Look Once: Unified, Real-Time Object Detection Object Detection: Performance Evaluation Metrics Intersection over Union (IoU) IoU (Intersection over Union) Calculation Intersection over Union (IoU) is a common metric used to evaluate the performance of an object detection algorithm. It measures the overlap between the predicted bounding box (P) and the ground truth bounding box (G). The IoU is calculated as the area of intersection divided by the area of union of P and G. The IoU score ranges from 0 to 1, where 0 indicates no overlap and 1 indicates a perfect match. A higher IoU score indicates a more accurate object detection. Average Precision (AP) Average Precision (AP) is another important metric used in object detection. It summarizes the precision-recall curve that is created by varying the detection threshold. Precision is the proportion of true positive detections among all positive detections, while recall is the proportion of true positive detections among all actual positives in the image. The AP computes the average precision values for recall levels over 0 to 1. The AP score ranges from 0 to 1, where a higher value indicates better performance. The mean Average Precision (mAP) is often used in practice, which calculates the AP for each class and then takes the average. By understanding these metrics, we can better interpret the performance of models like YOLO and make informed decisions about their application in real-world scenarios. After exploring various object detection methods and performance evaluation methods, let’s delve into the workings of a particularly powerful and popular algorithm known as ‘You Only Look Once’, or YOLO. This algorithm has revolutionized the field of object detection with its unique approach and impressive speed. Unlike traditional methods that involve separate steps for identifying objects and classifying them, YOLO accomplishes both tasks in a single pass, hence the name ‘You Only Look Once’. YOLO Object Detection Algorithm: How Does it Work? YOLO Architecture The YOLO algorithm employs a single Convolutional Neural Network (CNN) that divides the image into a grid. Each cell in the grid predicts a certain number of bounding boxes. Along with each bounding box, the cell also predicts a class probability, which indicates the likelihood of a specific object being present in the box. Convolution Layers Bounding Box Recognition Process The bounding box recognition process in YOLO involves the following steps: Grid Creation: The image is divided into an SxS grid. Each grid cell is responsible for predicting an object if the object’s center falls within it. Bounding Box Prediction: Each grid cell predicts B bounding boxes and confidence scores for those boxes. The confidence score reflects how certain the model is that a box contains an object and how accurate it thinks the box is. Class Probability Prediction: Each grid cell also predicts C conditional class probabilities (one per class for the potential objects). These probabilities are conditioned on there being an object in the box. YOLO Structure Non-Max Suppression (NMS) After the bounding boxes and class probabilities are predicted, post-processing steps are applied. One such step is Non-Max Suppression (NMS). NMS helps in reducing the number of overlapping bounding boxes. It works by eliminating bounding boxes that have a high overlap with the box that has the highest confidence score. Vector Generalization Vector generalization is a technique used in the YOLO algorithm to handle the high dimensionality of the output. The output of the YOLO algorithm is a tensor that contains the bounding box coordinates, objectness score, and class probabilities. This high-dimensional tensor is flattened into a vector to make it easier to process. The vector is then passed through a softmax function to convert the class scores into probabilities. The final output is a vector that contains the bounding box coordinates, objectness score, and class probabilities for each grid cell. Evolution of YOLO: YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOR, YOLOX, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9 If you are not interested in a quick recap of the timeline of YOLO models and the updates in the network architecture, skip this section! YOLOv1: First Real-time Object Detection Algorithm The original YOLO model treated object detection as a regression problem, which was a significant shift from the traditional classification approach. It used a single convolutional neural network (CNN) to detect objects in images by dividing the image into a grid, making multiple predictions per grid cell, filtering out low-confidence predictions, and then removing overlapping boxes to produce the final output. YOLOv2 [YOLO9000]: Multi-Scale Training| Anchor Boxes| Darknet-19 Backbone YOLOv2 introduced several improvements over the original YOLO. It used batch normalization in all its convolutional layers, which reduced overfitting and improved model stability and performance. It could handle higher-resolution images, making it better at spotting smaller objects. YOLOv2 also used anchor boxes (borrowed from Faster R-CNN), which helped the algorithm predict the shape and size of objects more accurately. YOLOv3: Three YOLO Layers| Logistic Classifiers| Upsampling |Darknet-53 Backbone Upsampling YOLOv3 introduced a new backbone network, Darknet-53, which utilized residual connections. It also made several design changes to improve accuracy while maintaining speed. At 320x320 resolution, YOLOv3 ran in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. It achieved 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, with similar performance but 3.8x faster. YOLOv4: CSPDarknet53 | Detection Across Scales | CIOU Loss Speed Comparison: YOLOv4 Vs. YOLOv3 YOLOv4 introduced several new techniques to improve both accuracy and speed. It used a CSPDarknet backbone and introduced new techniques such as spatial attention, Mish activation function, and GIoU loss to improve accuracy3. The improved YOLOv4 algorithm showed a 0.5% increase in average precision (AP) compared to the original algorithm while reducing the model’s weight file size by 45.3 M. YOLOR: Unified Network Architecture | Mosaic | Mixup | SimOTA UNA (Unified Network Architecture) Unlike previous YOLO versions, YOLOR’s architecture and model infrastructure differ significantly. The name “YOLOR” emphasizes its unique approach: it combines explicit and implicit knowledge to create a unified network capable of handling multiple tasks with a single input. By learning just one representation, YOLOR achieves impressive performance in object detection. YOLOX YOLOX is an anchor-free object detection model that builds upon the foundation of YOLOv3 SPP with a Darknet53 backbone. It aims to surpass the performance of previous YOLO versions. The key innovation lies in its decoupled head and SimOTA approach. By eliminating anchor boxes, YOLOX simplifies the design while achieving better accuracy. It bridges the gap between research and industry, offering a powerful solution for real-time object detection. YOLOX comes in various sizes, from the lightweight YOLOX-Nano to the robust YOLOX-x, each tailored for different use cases. YOLOv5: PANet| CSPDarknet53| SAM Block YOLOv5 brought about further enhancements to increase both precision and efficiency. It adopted a Scaled-YOLOv4 backbone and incorporated new strategies such as CIOU loss and CSPDarknet53-PANet-SPP to boost precision. Structure of YOLOv5 The refined YOLOv5 algorithm demonstrated a 0.7% rise in mean average precision (mAP) compared to the YOLOv4, while decreasing the model’s weight file size by 53.7 M. These improvements made YOLOv5 a more effective and efficient tool for real-time object detection. YOLOv6: EfficientNet-Lite | CSPDarknet-X backbone | Swish Activation Function | DIoU Loss YOLOv6 utilized a CSPDarknet-X backbone and introduced new methods such as panoptic segmentation, Swish activation function, and DIoU loss to boost accuracy. Framework of YOLOv6 The enhanced YOLOv6 algorithm exhibited a 0.8% increase in average precision (AP) compared to the YOLOv5, while shrinking the model’s weight file size by 60.2 M. These advancements made YOLOv6 an even more powerful tool for real-time object detection. YOLOv7: Leaky ReLU Activation Function| TIoU Loss| CSPDarknet-Z Backbone YOLOv7 employed a CSPDarknet-Z backbone in the yolov7 architecture. YOLOv7 object detection algorithm was enhanced by the introduction of innovative techniques such as object-centric segmentation, Leaky ReLU activation function, and TIoU loss to enhance accuracy. The advanced YOLOv7 algorithm demonstrated a 1.0% increase in average precision (AP) compared to the YOLOv6, while reducing the model’s weight file size by 70.5 M. These improvements made YOLOv7 object detection algorithm, an even more robust tool for real-time object detection. YOLOv8: Multi-Scale Object Detection| CSPDarknet-AA| ELU Activation Function| GIoU Loss YOLOv8 introduced a new backbone architecture, the CSPDarknet-AA, which is an advanced version of the CSPDarknet series, known for its efficiency and performance in object detection tasks. One key technique introduced in YOLOv8 is multi-scale object detection. This technique allows the model to detect objects of various sizes in an image. Another significant enhancement in YOLOv8 is the use of the ELU activation function. ELU, or Exponential Linear Unit, helps to speed up learning in deep neural networks by mitigating the vanishing gradient problem, leading to faster convergence. YOLOv8 adopted the GIoU loss. GIoU, or Generalized Intersection over Union, is a more advanced version of the IoU (Intersection over Union) metric that takes into account the shape and size of the bounding boxes, improving the precision of object localization. The YOLOv8 algorithm shows a 1.2% increase in average precision (AP) compared to the YOLOv7, which is a significant improvement. It has achieved this while reducing the model’s weight file size by 80.6 M, making the model more efficient and easier to deploy in resource-constrained environments. YOLOv8 Comparison with Latest YOLO models YOLOv9: GELAN Architecture| Programmable Gradient Information (PGI) YOLOv9 which was recently released overcame information loss challenges inherent in deep neural networks. By integrating PGI and the versatile GELAN architecture, YOLOv9 not only enhances the model’s learning capacity but also ensures the retention of crucial information throughout the detection process, thereby achieving exceptional accuracy and performance. Key Highlights of YOLOv9 Information Bottleneck Principle: This principle reveals a fundamental challenge in deep learning: as data passes through successive layers of a network, the potential for information loss increases. YOLOv9 counters this challenge by implementing Programmable Gradient Information (PGI), which aids in preserving essential data across the network’s depth, ensuring more reliable gradient generation and, consequently, better model convergence and performance. Reversible Functions: A function is deemed reversible if it can be inverted without any loss of information. YOLOv9 incorporates reversible functions within its architecture to mitigate the risk of information degradation, especially in deeper layers, ensuring the preservation of critical data for object detection tasks. For more information, read the blog YOLOv9: SOTA Object Detection Model Explained. YOLO Object Detection with Pre-Trained YOLOv9 on COCO Dataset Like all YOLO models, the pre-trained models of YOLOv9 is open-source and is available in GitHub. We are going to run our experiment on Google Colab. So if you are doing it on your local system, please bear in mind that the instructions and the code was made to run on Colab Notebook. Make sure you have access to GPU. You can either run the command below or navigate to Edit → Notebook settings → Hardware accelerator, set it to GPU, and the click Save. !nvidia-smi To make it easier to manage datasets, images, and models we create a HOME constant. import os HOME = os.getcwd() print(HOME) Clone and Install !git clone https://github.com/SkalskiP/yolov9.git %cd yolov9 !pip install -r requirements.txt -q Download Model Weights !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-c.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-e.pt Test Data Upload test image to the Colab notebook. !wget -P {HOME}/data -q –-add image path Detection with Pre-trained COCO Model on gelan-c !python detect.py --weights {HOME}/weights/gelan-c.pt --conf 0.1 --source image path --device 0 Evaluation of the Pre-trained COCO Model on gelan-c !python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './gelan-c.pt' --save-json --name gelan_c_640_val Performance of YOLOv9 on MS COCO Dataset Yolov9: Learning What You Want to Learn Using Programmable Gradient Information The performance of YOLOv9 on the MS COCO dataset exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. The smallest of the models, v9-S, achieved 46.8% AP on the validation set of the MS COCO dataset, while the largest model, v9-E, achieved 55.6% AP. This sets a new state-of-the-art for object detection performance. These results demonstrate the effectiveness of YOLOv9’s techniques, such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN), in enhancing the model’s learning capacity and ensuring the retention of crucial information throughout the detection process. For more information, read the paper of Arxiv: Yolov9: Learning What You Want to Learn Using Programmable Gradient Information. Training YOLOv9 on Custom Dataset Here for training data, we will be curating a custom dataset on Encord platform. With Encord you can either curate and create your custom dataset or use the sandbox datasets already created on Encord Active platform. Select New Dataset to Upload Data You can name the dataset and add a description to provide information about the dataset. Annotate Custom Dataset Create an annotation project and attach the dataset and the ontology to the project to start annotation with a workflow. You can choose manual annotation if the dataset is simple, small, and doesn’t require a review process. Automated annotation is also available and is very helpful in speeding up the annotation process. For more information on automated annotation, read the blog The Full Guide to Automated Data Annotation. Start Labeling The summary page shows the progress of the annotation project. The information regarding the annotators and the performance of the annotators can be found under the tabs labels and performance. Export the Annotation Once the annotation has been reviewed, export the annotation in the required format. For more information on exploring the quality of your custom dataset, read the blog Exploring the Quality of Hugging Face Image Datasets with Encord Active. You can use the custom dataset curated using Encord Annotate for training an object detection model. For testing YOLOv9, we are going to use an image from one of the sandbox projects on Encord Active. Copy and run the code below to run YOLOv9 for object detection. The code for using YOLOv9 for panoptic segmentation has also been made available now on the original GitHub repository. Installing YOLOv9 !git clone https://github.com/SkalskiP/yolov9.git %cd yolov9 !pip install -r requirements.txt -q !pip install -q roboflow encord av # This is a convenience class that holds the info about Encord projects and makes everything easier. # The class supports bounding boxes and polygons across both images, image groups, and videos. !wget 'https://gist.githubusercontent.com/frederik-encord/e3e469d4062a24589fcab4b816b0d6ec/raw/fa0bfb0f1c47db3497d281bd90dd2b8b471230d9/encord_to_roboflow_v1.py' -O encord_to_roboflow_v1.py Imports from typing import Literal from pathlib import Path from IPython.display import Image import roboflow from encord import EncordUserClient from encord_to_roboflow_v1 import ProjectConverter Download YOLOv9 Model Weights The YOLOv9 is available as 4 models which are ordered by parameter count: YOLOv9-S YOLOv9-M YOLOv9-C YOLOv9-E Here we will be using gelan-c. But the same process follows for other models. !mkdir -p {HOME}/weights !wget -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e-converted.pt -O {HOME}/weights/yolov9-e.pt !wget -P {HOME}/weights -q https://github.com/WongKinYiu/yolov9/releases/download/v0.1/gelan-c.pt Train Custom YOLOv9 Model for Object Detection !python train.py \ --batch 8 --epochs 20 --img 640 --device 0 --min-items 0 --close-mosaic 15 \ --data $dataset_yaml_file \ --weights {HOME}/weights/gelan-c.pt \ --cfg models/detect/gelan-c.yaml \ --hyp hyp.scratch-high.yaml For more information on end-to-end training YOLOv9 with custom dataset, check out the blog Comparative Analysis of YOLOv9 and YOLOv8 Using Custom Dataset on Encord Active. YOLO Object Detection using YOLOv9 on Custom Dataset In order to perform object detection, you have to run prediction of the trained YOLOv9 on custom dataset. Run Prediction import torch augment = False visualize = False conf_threshold = 0.25 nms_iou_thres = 0.45 max_det = 1000 seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device).float() im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim # Inference with dt[1]: pred = model(im, augment=augment, visualize=visualize)[0] # NMS with dt[2]: filtered_pred = non_max_suppression(pred, conf_threshold, nms_iou_thres, None, False, max_det=max_det) print(pred, filtered_pred) break Generate YOLOv9 Prediction on Custom Data import matplotlib.pyplot as plt from matplotlib.patches import Rectangle from PIL import Image img = Image.open(Image path) fig, ax = plt.subplots() ax.imshow(img) ax.axis("off") for p, c in zip(filtered_pred[0], ["r", "b", "g", "cyan"]): x, y, w, h, score, cls = p.detach().cpu().numpy().tolist() ax.add_patch(Rectangle((x, y), w, h, color="r", alpha=0.2)) ax.text(x+w/2, y+h/2, model.names[int(cls)], ha="center", va="center", color=c) fig.savefig("/content/predictions.jpg") YOLOv9 Vs YOLOv8: Comparative Analysis Using Encord You can convert the model predictions and upload them to Encord. Here for example, the YOLOv9 and YOLOv8 have been trained and compared on the Encord platform using the xView3 dataset, which contains aerial imagery with annotations for maritime object detection. The comparative analysis between YOLOv9 and YOLOv8 on the Encord platform focuses on precision, recall, and metric analysis. These metrics are crucial for evaluating the performance of object detection models. Precision: Precision measures the proportion of true positives (i.e., correct detections) among all detections. A higher precision indicates fewer false positives. Recall: Recall measures the proportion of actual positives that are correctly identified. A higher recall indicates fewer false negatives. Metric Analysis: This involves analyzing various metrics like Average Precision (AP), Mean Average Precision (mAP), etc., which provide a comprehensive view of the model’s performance. For example, in the precision-recall curve, it seems that YOLOv8 surpasses YOLOv9 in terms of the Area Under the Curve (AUC-PR) value. This suggests that, across various threshold values, YOLOv8 typically outperforms YOLOv9 in both precision and recall. It implies that YOLOv8 is more effective at correctly identifying true positives and reducing false positives compared to YOLOv9. But it is important to keep in mind that these two models which are being evaluated were trained for 20 epochs and are used as an example to show how to perform evaluation of trained models on custom datasets. For detailed information on performing a comparative analysis of trained models, read the blog Comparative Analysis of YOLOv9 and YOLOv8 Using Custom Dataset on Encord Active. YOLO Real-Time Implementation YOLO (You Only Look Once) models are widely used in real-time object detection tasks due to their speed and accuracy. Here are some real-world applications of YOLO models: Healthcare: YOLO models can be used in healthcare for tasks such as identifying diseases or abnormalities in medical images. Agriculture: YOLO models have been used to detect and classify crops, pests, and diseases, assisting in precision agriculture techniques and automating farming processes. Security Surveillance: YOLO models are used in security surveillance systems for real-time object detection, tracking, and classification. Self-Driving Cars: In autonomous vehicles, YOLO models are used for detecting objects such as other vehicles, pedestrians, traffic signs, and signals in real-time. Face Detection: They have also been adapted for face detection tasks in biometrics, security, and facial recognition systems YOLO Object Detection: Key Takeaways In this article, we provided an overview of the evolution of YOLO, from YOLOv1 to YOLOv8, and discussed its network architecture, new features, and applications. Additionally, we provided a step-by-step guide on how to use YOLOv8 for object detection and how to create model-assisted annotations with Encord Annotate. At Encord, we help computer vision companies build better models and training datasets. We have built an end-to-end Active Learning Platform for AI-assisted annotation workflows evaluating and evaluating your training data Orchestrating active learning pipelines Fixing data and label errors Diagnosing model errors & biases. Encord integrates the new YOLOv8 state-of-the-art model and allows you to train Micro-models on a backbone of YOLOv8 models to support your AI-assisted annotation work.
April 4
7 min
Announcing the launch of Consensus in Encord Workflows
At Encord, we continually obsess over how to support ML teams managing their labeling workflows and make it as easy as possible for teams to improve model performance. Today, we’re announcing the launch of Consensus workflows within Encord. What is Consensus? Consensus allows multiple annotators to conduct a labeling task of the same file in a mutually blind fashion — that is, each annotator is unaware that other annotators are working on the task. All submissions are aggregated into the following evaluation substage where designated Consensus reviewers can evaluate the agreement between labels and select a representative set. Integrating Consensus into your labeling workflows allows you to create higher-quality annotations by assessing the submissions of multiple annotators and simplifying compliance with domain-specific regulatory requirements. Support within Encord Support will begin with image and video modalities, with full modality support progressively released soon after. You can read more in our documentation for more information on activating this feature and building consensus workflows.
April 2
2 min
Visualizations in Databricks
With data becoming a pillar stone of a company’s growth strategy, the market for visualization tools is growing rapidly, with a projected compound annual growth rate (CAGR) of 10.07% between 2023 and 2028. The primary driver of these trends is the need for data-driven decision-making, which involves understanding complex data patterns and extracting actionable insights to improve operational efficiency. PowerBI and Tableau are traditional tools with interactive workspaces for creating intuitive dashboards and exploring large datasets. However, other platforms are emerging to address the ever-changing nature of the modern data ecosystem. In this article, we will discuss the visualizations offered by Databricks - a modern enterprise-scale platform for building data, analytics, and artificial intelligence (AI) solutions. Databricks Databricks is an end-to-end data management and model development solution built on Apache Spark. It lets you create and deploy the latest generative AI (Gen AI) and large language models (LLMs). The platform uses a proprietary Mosaic AI framework to streamline the model development process. It provides tools to fine-tune LLMs seamlessly through enterprise data and offers a unified service for experimentation through foundation models. In addition, it features Databricks SQL, a state-of-the-art lakehouse for cost-effective data storage and retrieval. It lets you centrally store all your data assets in an open format, Delta Lake, for effective governance and discoverability. Further, Databricks SQL has built-in support for data visualization, which lets you extract insights from datasets directly from query results in the SQL editor. Users also benefit from the visualization tools featured in Databricks Notebooks, which help you build interactive charts by using the Plotly library in Python. Through these visualizations, Databricks offers robust data analysis for monitoring data assets critical to your AI models. So, let’s discuss in more detail the types of chart visualizations, graphs, diagrams, and maps available on Databricks to help you choose the most suitable visualization type for your use case. Effective visualization can help with effortless data curation. Learn more about how you can use data curation for computer vision Visualizations in Databricks As mentioned earlier, Databricks provides visualizations through Databricks SQL and Databricks Notebooks. The platform lets you run multiple SQL queries to perform relevant aggregations and apply filters to visualize datasets according to your needs. Databricks also allows you to configure settings related to the X and Y axes, legends, missing values, colors, and labels. Users can also download visualizations in PNG format for documentation purposes. The following sections provide an overview of the various visualization types available in these two frameworks, helping you select the most suitable option for your project. Bar Chart Bar charts are helpful when you want to compare the frequency of occurrence of different categories in your dataset. For instance, you can draw a bar chart to compare the frequency of various age groups, genders, ethnicities, etc. Additionally, bar charts can be used to view the sum of the prices of all orders placed in a particular month and group them by priority. Bar chart The result will show the months on the X-axis and the sum of all the orders categorized by priority on the Y-axis. Line Line charts connect different data points through straight lines. They are helpful when users want to analyze trends over some time. The charts usually show time on the X-axis and some metrics whose trajectory you want to explore on the Y-axis. Line chart For instance, you can view changes in the average price of orders over the years grouped by priority. The trends can help you predict the most likely future values, which can help you with financial projections and budget planning. Pie Chart Pie charts display the proportion of different categories in a dataset. They divide a circle into multiple segments, each showing the proportion of a particular category, with the segment size proportional to the category’s percentage of the total. Pie chart For instance, you can visualize the proportion of orders for each priority. The visualization is helpful when you want a quick overview of data distribution across different segments. It can help you analyze demographic patterns, market share of other products, budget allocation, etc. Scatter Plot A scatter plot displays each data point as a dot representing a relationship between two variables. Users can also control the color of each dot to reflect the relationship across different groups. Scatter Plot For instance, you can plot the relationship between quantity and price for different color-coded item categories. The visualization helps in understanding the correlation between two variables. However, users must interpret the relationship cautiously, as correlation does not always imply causation. Deeper statistical analysis is necessary to uncover causal factors. Area Charts Area charts combine line and bar charts by displaying lines and filling the area underneath with colors representing particular categories. They show how the contribution of a specific category changes relative to others over time. Area Charts For instance, you can visualize which type of order priority contributed the most to revenue by plotting the total price of different order priorities across time. The visualization helps you analyze the composition of a specific metric and how that composition varies over time. It is particularly beneficial in analyzing sales growth patterns for different products, as you can see which product contributed the most to growth across time. Box Chart Box charts concisely represent data distributions of numerical values for different categories. They show the distribution’s median, skewness, interquartile, and value ranges. Box Chart For instance, the box can display the median price value through a line inside the box and the interquartile range through the top and bottom box enclosures. The extended lines represent minimum and maximum price values to compute the price range. The chart helps determine the differences in distribution across multiple categories and lets you detect outliers. You can also see the variability in values across different categories and examine which category was the most stable. Bubble Chart Bubble charts enhance scatter plots by allowing you to visualize the relationship of three variables in a two-dimensional grid. The bubble position represents how the variable on the X-axis relates to the variable on the Y-axis. The bubble size represents the magnitude of a third variable, showing how it changes as the values of the first two variables change. Bubble chart The visualization is helpful for multi-dimensional datasets and provides greater insight when analyzing demographic data. However, like scatter plots, users must not mistake correlation for causation. Combo Chart Combo charts combine line and bar charts to represent key trends in continuous and categorical variables. The categorical variable is on the X-axis, while the continuous variable is on the Y-axis. Combo Chart For instance, you can analyze how the average price varies with the average quantity according to shipping date. The visualization helps summarize complex information involving relationships between three variables on a two-dimensional graph. However, unambiguous interpretation requires careful configuration of labels, colors, and legends. Heatmap Chart Heatmap charts represent data in a matrix format, with each cell having a different color according to the numerical value of a specific variable. The colors change according to the value intensity, with lower values typically having darker and higher values having lighter colors. Heatmap chart For instance, you can visualize how the average price varies according to order priority and order status. Heatmaps are particularly useful in analyzing correlation intensity between two variables. They also help detect outliers by representing unusual values through separate colors. However, interpreting the chart requires proper scaling to ensure colors do not misrepresent intensities. Histogram Histograms display the frequency of particular value ranges to show data distribution patterns. The X-axis contains the value ranges organized as bins, and the Y-axis shows the frequency of each bin. Histogram For instance, you can visualize the frequency of different price ranges to understand price distribution for your orders. The visualization lets you analyze data spread and skewness. It is beneficial in deeper statistical analysis, where you want to derive probabilities and build predictive models. Pivot Tables Pivot tables can help you manipulate tabular displays through drag-and-drop options by changing aggregation records. The option is an alternative to SQL filters for viewing aggregate values according to different conditions. Pivot Tables For instance, you can group total orders by shipping mode and order category. The visualization helps prepare ad-hoc reports and provides important summary information for decision-making. Interactive pivot tables also let users try different arrangements to reveal new insights. Choropleth Map Visualization Choropleth map visualization represents color-coded aggregations categorized according to different geographic locations. Regions with higher value intensities have darker colors, while those with lower intensities have lighter shades. Choropleth map visualization For instance, you can visualize the total revenue coming from different countries. This visualization helps determine global presence and highlight disparities across borders. The insights will allow you to develop marketing strategies tailored to regional tastes and behavior. Funnel Visualization Funnel visualization depicts data aggregations categorized according to specific steps in a pipeline. It represents each step from top to bottom with a bar and the associated value as a label overlay on each bar. It also displays cumulative percentage values showing the proportion of the aggregated value resulting from each stage. Funnel Visualization For instance, you can determine the incoming revenue streams at each stage of the ordering process. This visualization is particularly helpful in analyzing marketing pipelines for e-commerce sites. The tool shows the proportion of customers who view a product ad, click on it, add it to the cart, and proceed to check out. Cohort Analysis Cohort analysis offers an intuitive visualization to track the trajectory of a particular metric across different categories or cohorts. Cohort Analysis For instance, you can analyze the number of active users on an app that signed up in different months of the year. The rows will depict the months, and the columns will represent the proportion of active users in a particular cohort as they move along each month. The visualization helps in retention analysis as you can determine the proportion of retained customers across the user lifecycle. Counter Display Databricks allows you to configure a counter display that explicitly shows how the current value of a particular metric compares with the metric’s target value. Counter display For instance, you can check how the average total revenue compares against the target value. In Databricks, the first row represents the current value, and the second is the target. The visualization helps give a quick snapshot of trending performance and allows you to quantify goals for better strategizing. Sankey Diagrams Sankey diagrams show how data flows between different entities or categories. It represents flows through connected links representing the direction, with entities displayed as nodes on either side of a two-dimensional grid. The width of the connected links represents the magnitude of a particular value flowing from one entity to the other. Sankey Diagram For instance, you can analyze traffic flows from one location to the other. Sankey diagrams can help data engineering teams analyze data flows from different platforms or servers. The analysis can help identify bottlenecks, redundancies, and resource constraints for optimization planning. Sunburst Sequence The sunburst sequence visualizes hierarchical data through concentric circles. Each circle represents a level in the hierarchy and has multiple segments. Each segment represents the proportion of data in the hierarchy. Furthermore, it color codes segments to distinguish between categories within a particular hierarchy. Sunburst Sequence For instance, you can visualize the population of different world regions through a sunburst sequence. The innermost circle represents a continent, the middle one shows a particular region, and the outermost circle displays the country within that region. The visualization helps data science teams analyze relationships between nested data structures. The information will allow you to define clear data labels needed for model training. Table A table represents data in a structured format with rows and columns. Databricks offers additional functionality to hide, reformat, and reorder data. Tables help summarize information in structured datasets. You can use them for further analysis through SQL queries. Word Cloud Word cloud visualizations display words in different sizes according to their frequency in textual data. For instance, you can analyze customer comments or feedback and determine overall sentiment based on the highest-occurring words. Word Cloud While word clouds help identify key themes in unstructured textual datasets, they can suffer from oversimplification. Users must use word clouds only as a quick overview and augment textual analysis with advanced natural language processing techniques. Visualization is critical to efficient data management. Find out the top tools for data management for computer vision Visualizations in Databricks: Key Takeaways With an ever-increasing data volume and variety, visualization is becoming critical for quickly communicating data-based insights in a simplified manner. Databricks is a powerful tool with robust visualization types for analyzing complex datasets. Below are a few key points to remember regarding visualization in Databricks. Databricks SQL and Databricks Notebooks: Databricks offers advanced visualizations through Databricks SQL and Databricks Notebooks as a built-in functionality. Visualization configurations: Users can configure multiple visualization settings to produce charts, graphs, maps, and diagrams per their requirements. Visualization types: Databricks offers multiple visualizations, including bar charts, line graphs, pie charts, scatter plots, area graphs, box plots, bubble charts, combo charts, heatmaps, histograms, pivot tables, choropleth maps, funnels, cohort tables, counter display, Sankey diagrams, sunburst sequences, tables, and word clouds.
March 28
10 min
Data Lake Explained: A Comprehensive Guide for ML Teams
What is a Data Lake? A data lake is a centralized repository where you can store all your structured, semi-structured, and unstructured data types at any scale for processing, curation, and analytics. It supports batch and real-time streams to combine raw data from diverse sources (databases, IoT devices, mobile apps, etc.) into the repository without a predefined schema. It has been 12 years since the New York Times published an interesting article on ‘The Age of Big Data,’ in which most of the talk and tooling were centered around analytics. Fast-forward to today, and we are continuously grappling with the influx of data at the petabyte (PB) and zettabyte (ZB) scales, which is getting increasingly complex in dimensions (images, videos, point cloud data, etc.). It is clear that solutions that can help manage the size and complexity of data are needed for organizational success. This has urged data, AI, and technology teams to look towards three pivotal data management solutions: data lakes, data warehouses, and cloud services. This article focuses on understanding data lakes as a data management solution for machine learning (ML) teams. You will learn: What a data lake is and how it differs from a data warehouse. Benefits and limitations of a data lake for ML teams. The data lake architecture. Best practices for setting up a data lake. On-premise vs. cloud-based data lakes. Computer vision use cases of data lakes. TL; DR A data lake is a centralized repository for diverse, structured, and unstructured data. Key architecture components include Data Sources, Data Ingestion, Data Persistence and Storage, Data Processing Layer, Analytical Sandboxes, Data Lake Zones, and Data Consumption. Best practices for data lakes involve defining clear objectives, robust data governance, scalability, prioritizing security, encouraging a data-driven culture, and quality control. On-premises data lakes offer control and security; cloud-based data lakes provide scalability and cost efficiency. Data lakes are evolving with advanced analytics and computer vision use cases, emphasizing the need for adaptable systems and adopting forward-thinking strategies. Overview: Data Warehousing, Data Lake, and Cloud Storage Data Warehouses A data warehouse is a single location where an organization's structured data is consolidated, transformed, and stored for query and analysis. The structured data is ideal for generating reports and conducting analytics that inform business decisions. Limitations Limited agility in handling unstructured or semi-structured data. Can create data silos, hindering cross-departmental data sharing. Data Lakes A data lake stores vast amounts of raw datasets in their native format until needed, which includes structured, semi-structured, and unstructured data. This flexibility supports diverse applications, from computer vision use cases to real-time analytics. Challenges Risk of becoming a "data swamp" if not properly managed, with unclear, unclean, or redundant data. Requires robust metadata and governance practices to ensure data is findable and usable. Cloud Storage and Computing Cloud computing encompasses a broad spectrum of services beyond storage, such as processing power and advanced analytics. Cloud storage refers explicitly to storing data on the internet through a cloud computing provider that manages and operates data storage as a service. Risks Security concerns, requiring stringent data access controls and encryption. Potential for unexpected costs if usage is not monitored. Dependence on the service provider's reliability and continuity. Data lake overview with the data being ingested from different sources. Most ML teams misinterpret the role of data lakes and data warehouses, choosing an inappropriate management solution. Before delving into the rest of the article, let’s clarify how they differ. Data Lake vs. Data Warehouse Understanding the strengths and use cases of data lakes and warehouses can help your organization maximize its data assets. This can help create an efficient data infrastructure that supports various analytics, reporting, and ML needs. Let’s compare a data lake to a data warehouse based on specific features. Choosing Between Data Lake and Data Warehouse The choice between a data lake and a warehouse depends on the specific needs of the analysis. For an e-commerce organization analyzing structured sales data, a data warehouse offers the speed and efficiency required for such tasks. However, a data lake (or a combination of both solutions) might be more appropriate for applications that require advanced computer vision (CV) techniques and large visual datasets (images, videos). Benefits of a Data Lake Data lakes offer myriad benefits to organizations using complex datasets for analytical insights, ML workloads, and operational efficiency. Here's an overview of the key benefits: Single Source of Truth: When you centralize data in data lakes, you get rid of data silos, which makes data more accessible across the whole organization. So, data lakes ensure that all the data in an organization is consistent and reliable by providing a single source of truth. Schema on Read: Unlike traditional databases that define data structure at write time (schema on write), data lakes allow the structure to be imposed at read time to offer flexibility in data analysis and utilization. Scalability and Cost-Effectiveness: Data lakes' cloud-based nature facilitates scalable storage solutions and computing resources, optimizing costs by reducing data duplication. Decoupling of Storage and Compute: Data lakes let different programs access the same data without being dependent on each other. This makes the system more flexible and helps it use its resources more efficiently. Architectural Principles for Data Lake Design When designing a data lake, consider these foundational principles: Decoupled Architecture: Data ingestion, processing, curation, and consumption should be independent to improve system resilience and adaptability. Tool Selection: Choose the appropriate tools and platforms based on data characteristics, ingestion, and processing requirements, avoiding a one-size-fits-all approach. Data Temperature Awareness: Classify data as hot (frequently accessed), warm (less frequently accessed), or cold (rarely accessed but retained for compliance) to optimize storage strategies and access patterns based on usage frequency. Leverage Managed Services: Use managed or serverless services to reduce operational overhead and focus on value-added activities. Immutability and Event Journaling: Design data lakes to be immutable, preserving historical data integrity and supporting comprehensive data analysis. They should also store and version the data labels. Cost-Conscious Design: Implement strategies (balancing performance, access needs, budget constraints) to manage and optimize costs without compromising data accessibility or functionality. Data Lake Architecture A robust data lake architecture is pivotal for harnessing the power of large datasets so organizations can store, process, and analyze them efficiently. This architecture typically comprises several layers dedicated to a specific function within the data management ecosystem. Below is an overview of these key components: Data Sources Diverse Producers: Data lakes can ingest data from a myriad of sources, including, but not limited to, IoT devices, cameras, weblogs, social media, mobile apps, transactional databases (SQL, NoSQL), and external APIs. This inclusivity enables a holistic view of business operations and customer interactions. Multiple Formats: They accommodate a wide range of data formats, from structured data in CSVs and databases to unstructured data like videos, images, DICOM files, documents, and multimedia files, providing a unified repository for all organizational data. This, of course, does not exclude semi-structured data like XML and JSON files. Data Ingestion Batch and Streaming: Data ingestion mechanisms in a data lake architecture support batch and real-time data flows. Use tools and services to auto-ingest the data so the system can effectively capture it. Validation and Metadata: Data is tagged with metadata during ingestion for easy retrieval, and initial validation checks are performed to ensure data quality and integrity. Data Governance Zone Access Control and Auditing: Implementing robust access controls, encryption, and auditing capabilities ensures data security and privacy, crucial for maintaining trust and compliance. Metadata Management: Documenting data origins, formats, lineage, ownership, and usage history is central to governance. This component incorporates tools for managing metadata, which facilitates data discovery, lineage tracking, and cataloging, enhancing the usability and governance of the data lake. Data Persistence and Staging Raw Data Storage: Data is initially stored in a staging area in raw, unprocessed form. This approach ensures that the original data is preserved for future processing needs and compliance requirements. Staging Area: Data may be staged or temporarily held in a dedicated area within the lake before processing. To efficiently handle the volume and variety of data, this area is built on scalable storage technologies, such as HDFS (Hadoop Distributed File System) or cloud-based storage services like Amazon S3. Data Processing Layer Transformation and Enrichment: This layer transforms data into a more usable format, often involving data cleaning, enrichment, deduplication, anonymization, normalization, and aggregation processes. It also improves data quality and ensures reliability for downstream analysis. Processing Engines: To cater to various processing needs, the architecture should support multiple processing engines, such as Hadoop for batch processing, Spark for in-memory processing, and others for specific tasks like stream processing. Data Indexing: This component indexes processed data to facilitate faster search and retrieval. It is crucial for supporting efficient data exploration and curation. Related: Interested in learning the techniques and best data cleaning and preprocessing practices? Check out one of our most-read guides, “Mastering Data Cleaning & Data Preprocessing.” Data Quality Monitoring Continuous Quality Checks: Implements automated processes for continuous monitoring of data quality, identifying issues like inconsistencies, duplications, or anomalies to maintain the accuracy, integrity, and reliability of the data lake. Quality Metrics and Alerts: Define and track data quality metrics, set up alert mechanisms for when data quality thresholds are breached, and enable proactive issue resolution. Related: Read how you can automate the assessment of training data quality in this article. Analytical Sandboxes Exploration and Experimentation: Computer vision engineers and data scientists can use analytical sandboxes to experiment with data sets, build models, and visually explore data (e.g., images, videos) and embeddings without impacting the integrity of the primary data (versioned data and labels). Tool Integration: These sandboxes support a wide range of analytics, data, and ML tools, giving users the flexibility and choice to work with their preferred technologies. Worth Noting: Building computer vision applications? Encord Active integrates with Annotate (with cloud platform integrations) and provides explorers with a way to explore image embeddings for any scale of data visually. See how to use it in the docs. Data Consumption Access and Integration: Data stored in the data lake is accessible to various downstream applications and users, including BI tools, reporting systems, computer vision platforms, or custom applications. This accessibility ensures that insights from the data lake can drive decision-making across the organization. APIs and Data Services: For programmatic access, APIs and data services enable developers and applications to query and retrieve data from the data lake, integrating data-driven insights into business processes and applications. Best Practices for Setting Up a Data Lake Implementing a data lake requires careful consideration and adherence to best practices to be successful and sustainable. Here are some suggested best practices to help you set up a data lake that can grow with your organization’s changing and growing data needs: #1. Define Clear Objectives and Scope Understand Your Data Needs: Before setting up a data lake, identify the types of data you plan to store, the insights you aim to derive, and the stakeholders who will consume this data. This understanding will guide your data lake's design, architecture, and governance model. Set Clear Objectives: Establish specific, measurable objectives for your data lake, such as improving data accessibility for analytics, supporting computer vision projects, or consolidating disparate data sources. These objectives will help prioritize features and guide decision-making throughout the setup process. #2. Ensure Robust Data Governance Implement a Data Governance Framework: A strong governance framework is essential for maintaining data quality, managing access controls, and ensuring compliance with regulatory standards. This framework should include data ingestion, storage, management, and archival policies. Metadata Management: Cataloging data with metadata is crucial for making it discoverable (indexing, filtering, sorting) and understandable. Implement tools and processes to automatically capture metadata, including data source, tags, format, and access permissions, during ingestion or at rest. Metadata can be technical (data design; schema, tables, formats, source documentation), business (docs on usage), and operational (events, access history, trace logs). #3. Focus on Scalability and Flexibility Choose Scalable Infrastructure: Whether on-premises or cloud-based, ensure your data lake infrastructure can scale to accommodate future data growth without significant rework or additional investment. Plan for Varied Data Types: Design your data lake to handle structured, semi-structured, and unstructured data. Flexibility in storing and processing different data types (images, videos, DICOM, blob files, etc.) ensures the data lake can support a wide range of use cases. #4. Prioritize Security and Compliance Implement Strong Security Measures: Security is paramount for protecting sensitive data and maintaining user trust. Apply encryption in transit and at rest, manage access with role-based controls, and regularly audit data access and usage. Compliance and Data Privacy: Consider the legal and regulatory requirements relevant to your data. Incorporate compliance controls into your data lake's architecture and operations, including data retention policies and the right to be forgotten. #5. Foster a Data-Driven Culture Encourage Collaboration: Promote collaboration between software engineers, CV engineers, data scientists, and analysts to ensure the data lake meets the diverse needs of its users. Regular feedback loops can help refine and enhance the data lake's utility. Education and Training: Invest in stakeholder training to maximize the data lake's value. Understanding how to use the data lake effectively can spur innovation and lead to new insights across the organization. #6. Continuous Monitoring and Optimization Monitor Data Lake Health: Regularly monitor the data lake for performance, usage patterns, and data quality issues. This proactive approach can help identify and resolve problems before they impact users. Iterate and Optimize: Your organization's needs will evolve, and so will your data lake. Continuously assess its performance and utility, adjusting based on user feedback and changing business requirements. Cloud-based Data Lake Platforms Cloud-based data lake platforms offer scalable, flexible, and cost-effective solutions for storing and analyzing large amounts of data. These platforms provide Data Lake as a Service (DLaaS), which simplifies the setup and management of data lakes. This allows organizations to focus on deriving insights rather than infrastructure management. Let's explore the architecture of data lake platforms provided by AWS, Azure, Snowflake, GCP, and their applications in multi-cloud environments. AWS Data Lake Architecture Amazon Web Services (AWS) provides a comprehensive and mature set of services to build a data lake. The core components include: Ingestion: AWS Glue for ETL processes and AWS Kinesis for real-time data streaming. Storage: Amazon S3 for scalable and secure data storage. Processing and Analysis: Amazon EMR is used for big data processing, AWS Glue for data preparation and loading, and Amazon Redshift for data warehousing. Consumption: Send your curated data to AWS SageMaker to run ML workloads or Amazon QuickSight to build visualizations, perform ad-hoc analysis, and quickly get business insights from data. Security and Governance: AWS Lake Formation automates the setup of a secure data lake, manages data access and permissions, and provides a centralized catalog for discovering and searching for data. Azure Data Lake Architecture Azure's data lake architecture is centered around Azure Data Lake Storage (ADLS) Gen2, which combines the capabilities of Azure Blob Storage and ADLS Gen1. It offers large-scale data storage with a hierarchical namespace and a secure HDFS-compatible data lake. Ingestion: Azure Data Factory for ETL operations and Azure Event Hubs for real-time event processing. Storage: ADLS Gen2 for a highly scalable data lake foundation. Processing and Consumption: Azure Databricks for big data analytics running on Apache Spark, Azure Synapse Analytics for querying (SQL serverless) and analysis (Notebooks), and Azure HDInsight for Hadoop-based services. Power BI can connect to ADLS Gen2 directly to create interactive reports and dashboards. Security and Governance: Azure provides fine-grained access control with Azure Role-Based Access Control (RBAC) and secures data with Microsoft Entra ID. Snowflake Data Lake Architecture Snowflake's unique architecture separates compute and storage, allowing users to scale them independently. It offers a cloud-agnostic solution operating across AWS, Azure, and GCP. Ingestion: Within Snowflake, Snowpipe Streaming runs on top of Apache Kafka for real-time ingestion. Apache Kafka acts as the messaging broker between the source and Snowlake. You can run batch ingestion with Python scripts and the PUT command. Storage: Uses cloud provider's storage (S3, ADLS, or Google Cloud Storage) or internal (i.e., Snowflake) stages to store structured, unstructured, and semi-structured data in their native format. Processing and Curation: Snowflake's Virtual Warehouses provide dedicated compute resources for data processing for high performance and concurrency. Snowpark can implement business logic within existing programming languages. Data Sharing and Governance: Snowflake enables secure data sharing between Snowflake accounts with governance features for managing data access and security. Consumption: Snowflake provides native connectors for popular BI and data visualization tools, including Google Analytics and Looker. Snowflake Marketplace provides users access to a data marketplace to discover and access third-party data sets and services. Snowpark helps with features for end-to-end ML. High-level architecture for running data lake workloads using Snowpark in Snowflake Google Cloud Data Lake Architecture In addition to various processing and analysis services, Google Cloud Platform (GCP) bases its data lake solutions on Google Cloud Storage (GCS), the primary data storage service. Ingestion: Cloud Pub/Sub for real-time messaging Storage: GCS offers durable and highly available object storage. Processing: Cloud Data Fusion offers pre-built transformations for batch and real-time processing, and Dataflow is for serverless stream and batch data processing. Consumption and Analysis: BigQuery provides serverless, highly scalable data analysis with an SQL-like interface. Dataproc runs Apache Hadoop and Spark jobs. Vertex AI provides machine learning capabilities to analyze and derive insights from lake data. Security and Governance: Cloud Identity and Access Management (IAM) controls resource access, and Cloud Data Loss Prevention (DLP) helps discover and protect sensitive data. Data Lake Architecture on Multi-Cloud Multi-cloud data lake architectures leverage services from multiple cloud providers, optimizing for performance, cost, and regulatory compliance. This approach often involves: Cloud-Agnostic Storage Solutions: Storing data in a manner accessible across cloud environments, either through multi-cloud storage services or by replicating data across cloud providers. Cross-Cloud Services Integration: This involves using best-of-breed services from different cloud providers for ingestion, processing, analysis, and governance, facilitated by data integration and orchestration tools. Unified Management and Governance: Implement multi-cloud management platforms to ensure consistent monitoring, security, and governance across cloud environments. Implementing a multi-cloud data lake architecture requires careful planning and robust data management strategies to ensure seamless operation, data consistency, and compliance across cloud boundaries. On-Premises Data Lakes and Cloud-based Data Lakes Organizations looking to implement data lakes have two primary deployment models to consider: on-premises and cloud-based (although more recent approaches involve a hybrid of both solutions). Cost, scalability, security, and accessibility affect each model's advantages and disadvantages. On-Premises Data Lakes: Advantages Control and Security: On-premises data lakes offer organizations complete control over their infrastructure, which can be crucial for industries with stringent regulatory and compliance requirements. This control also includes data security, so security measures can be tailored to each organization's needs. Performance: With data stored locally, on-premises solutions can provide faster data access and processing speeds, which is beneficial for time-sensitive applications that require rapid data retrieval and analysis. On-Premises Data Lakes: Challenges Cost and Scalability: Establishing an on-premises data lake requires a significant upfront investment in hardware and infrastructure. Scaling up can also require additional hardware purchases and be time-consuming. Maintenance: On-premises data lakes necessitate ongoing maintenance, including hardware upgrades, software updates, and security patches, which require dedicated IT staff and resources. Cloud-based Data Lakes: Advantages Scalability and Flexibility: Cloud-based data lakes can change their storage and computing power based on changing data volumes and processing needs without changing hardware. Cost Efficiency: A pay-as-you-go pricing model allows organizations to avoid substantial upfront investments and only pay for their storage and computing resources, potentially reducing overall costs. Innovative Features: Cloud service providers always add new technologies and features to their services, giving businesses access to the most advanced data management and analytics tools. Cloud-based Data Lakes: Challenges Data Security and Privacy: While cloud providers implement robust security measures, organizations may have concerns about storing sensitive data off-premises, particularly in industries with strict data sovereignty regulations. Dependence on Internet Connectivity: Access to cloud-based data lakes relies on stable internet connectivity. Any disruptions in connectivity can affect data access and processing, impacting operations. Understanding these differences enables organizations to select the most appropriate data lake solution to support their data management strategy and business objectives. Computer Vision Use Cases of Data Lakes Data lakes are pivotal in powering computer vision applications across various industries by providing a scalable repository for storing and analyzing vast large image and video datasets in real-time. Here are some compelling use cases where data lakes improve computer vision applications: Healthcare: Medical Imaging and Diagnosis In healthcare, data lakes store vast collections of medical images (e.g., X-rays, MRIs, CT scans, PET) that, combined with data curation tools, can improve image quality, detect anomalies, and provide quantitative assessments. CV algorithms analyze these images in real time to diagnose diseases, monitor treatment progress, and plan surgeries. Case Study: Viz.ai uses artificial intelligence to speed care and improve patient outcomes. In this case study, learn how they ingest, annotate, curate, and consume medical data. Autonomous Vehicles: Navigation and Safety Autonomous vehicle developers use data lakes to ingest and curate diverse datasets from vehicle sensors, including cameras, LiDAR, and radar. This data is crucial for training computer vision algorithms that enable autonomous driving capabilities, such as object detection, automated curb management, traffic sign recognition, and pedestrian tracking. Case Study: Automotus builds real-time curbside management automation solutions. Learn how they ingested raw, unlabeled data into Encord via Annotate and curated a balanced, diverse dataset with Active in this case study. How Automotus increased mAP 20% by reducing their dataset size by 35% with visual data curation Agriculture: Precision Farming In the agricultural sector, data lakes store and curate visual data (images and videos) captured by drones or satellites over farmland. Computer vision techniques analyze this data to assess crop health, identify pest infestations, and evaluate water usage, so farmers can make informed decisions and apply treatments selectively. Case Study: Automated harvesting and analytics company Four Growers uses Encord’s platform and annotators to help build its training datasets from scratch, labeling millions of instances of greenhouses and plants. Learn how the platform has halved the time it takes for them to build training data in this case study. Security and Surveillance: Threat Detection Government and private security agencies use data lakes to compile video feeds from CCTV cameras in public spaces, airports, and critical infrastructure. Real-time analysis with computer vision helps detect suspicious activities, unattended objects, and unauthorized entries, triggering immediate responses to potential security threats. ML Team's Data Lake Guide: Key Takeaways Data lakes have become essential for scalable storage and processing of diverse data types in modern data management. They facilitate advanced analytics, including real-time applications like computer vision. Their ability to transform sectors ranging from finance to agriculture by enhancing operational efficiencies and providing actionable insights makes them invaluable. As we look ahead: The continuous evolution of data lake architectures, especially within cloud-native and multi-cloud contexts, promises to bring forth advanced tools and services for improved data handling. This progression presents an opportunity for enterprises to transition from viewing data lakes merely as data repositories to leveraging them as strategic assets capable of building advanced CV applications. To maximize data lakes, address the problems associated with data governance, security, and quality. This will ensure that data remains a valuable organizational asset and a catalyst for data-driven decision-making and strategy formulation.
March 28
11 min
MM1: Apple’s Multimodal Large Language Models (MLLMs)
What is MM1? MM1 is a family of large multimodal language models that combines text and image understanding. It boasts an impressive 30 billion parameters and excels in both pre-training and supervised fine-tuning. MM1 generates and interprets both images and text data, making it a powerful tool for various multimodal tasks. Additionally, it incorporates a mixture-of-experts (MoE) architecture, contributing to its state-of-the-art performance across benchmarks. Introduction to Multimodal AI Multimodal AI models are a type of artificial intelligence model that can process and generate multiple types of data, such as text, images, and audio. These models are designed to understand the world in a way that is closer to how humans do, by integrating information from different modalities. Multimodal AI models typically use a combination of different types of AI systems, each designed to process a specific type of data. For example, a multimodal AI model might use a convolutional neural network (CNN) to process visual data, a recurrent neural network (RNN) to process text data, and a transformer model to integrate the information from CNN and RNN. The outputs of these networks are then combined, often using techniques such as concatenation or attention mechanisms, to produce a final output. This output can be used for a variety of tasks, such as classification, generation, or prediction. Overview of Multimodal Large Language Models (MLLMs) Multimodal Large Language Models (MLLMs) are generative AI systems that combine different types of information, such as text, images, videos, audio, and sensory data, to understand and generate human-like language. These models revolutionize the field of natural language processing (NLP) by going beyond text-only models and incorporating a wide range of modalities. Here's an overview of key aspects of Multimodal Large Language Models: Architecture MLLMs typically extend architectures like Transformers, which have proven highly effective in processing sequential data such as text. Transformers consist of attention mechanisms that enable the model to focus on relevant parts of the input data. In MLLMs, additional layers and mechanisms are added to process and incorporate information from other modalities. Integration of Modalities MLLMs are designed to handle inputs from multiple modalities simultaneously. For instance, they can analyze both the text and the accompanying image in a captioning task or generate a response based on both text and audio inputs. This integration allows MLLMs to understand and generate content that is richer and more contextually grounded. Pre-Training Like their unimodal counterparts, MLLMs are often pre-trained on large datasets using self-supervised learning objectives. Pre-training involves exposing the model to vast amounts of multimodal data, allowing it to learn representations that capture the relationships between different modalities. Pre-training is typically followed by fine-tuning on specific downstream tasks. State-of-the-Art Models CLIP (Contrastive Language-Image Pre-training): Developed by OpenAI, CLIP learns joint representations of images and text by contrasting semantically similar and dissimilar image-text pairs. GPT-4: It showcases remarkable capabilities in complex reasoning, advanced coding, and even performs well in multiple academic exams. Kosmos-1: Created by Microsoft, this MLLM os trained from scratch on web-scale multimodal corpora, including arbitrary interleaved text and images, image-caption pairs, and text data. PaLM-E: Developed by Google, PaLM-E integrates different modalities to enhance language understanding. Understanding MM1 Models MM1 represents a significant advancement in the domain of Multimodal Large Language Models (MLLMs), demonstrating state-of-the-art performance in pre-training metrics and competitive results in various multimodal benchmarks. The development of MM1 stems from a meticulous exploration of architecture components and data choices, aiming to distill essential design principles for building effective MLLMs. MM1 Model Experiments: Key Research Findings Architecture Components Image Encoder: The image encoder's design, along with factors such as image resolution and token count, significantly impacts MM1's performance. Through careful ablations, it was observed that optimizing the image encoder contributes substantially to MM1's capabilities. Vision-Language Connector: While important, the design of the vision-language connector was found to be of comparatively lesser significance compared to other architectural components. It plays a crucial role in facilitating communication between the visual and textual modalities. Data Choices Pre-training Data: MM1 leverages a diverse mix of image-caption, interleaved image-text, and text-only data for pre-training. This combination proved pivotal in achieving state-of-the-art few-shot results across multiple benchmarks. The study highlights the importance of different types of pre-training data for various tasks, with caption data being particularly impactful for zero-shot performance. Supervised Fine-Tuning (SFT): The effectiveness of pre-training data choices was validated through SFT, where capabilities and modeling decisions acquired during pre-training were retained, leading to competitive performance across evaluations and benchmarks. Performance In-Context Learning Abilities: The MM1 model exhibits exceptional in-context learning abilities, particularly in its largest 30 billion parameter configuration. This version of the model can perform multi-step reasoning over multiple images using few-shot “chain-of-thought” prompting. Model Scale: MM1's scalability is demonstrated through the exploration of larger LLMs, ranging from 3B to 30B parameters, and the investigation of mixture-of-experts (MoE) models. This scalability contributes to MM1's adaptability to diverse tasks and datasets, further enhancing its performance and applicability. Performance: The MM1 models, which include both dense models and mixture-of-experts (MoE) variants, achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Apple MM1 Model’s Features In-Context Predictions The Apple MM1 model excels at making predictions within the context of a given input. By considering the surrounding information, it can generate more accurate and contextually relevant responses. For instance, when presented with a partial sentence or incomplete query, the MM1 model can intelligently infer the missing parts and provide meaningful answers. Multi-Image Reasoning The MM1 model demonstrates impressive capabilities in reasoning across multiple images. It can analyze and synthesize information from various visual inputs, allowing it to make informed decisions based on a broader context. For example, when evaluating a series of related images (such as frames from a video), the MM1 model can track objects, detect changes, and understand temporal relationships. Chain-of-Thought Reasoning One of the standout features of the MM1 model is its ability to maintain a coherent chain of thought. It can follow logical sequences, connect ideas, and provide consistent responses even in complex scenarios. For instance, when engaged in a conversation, the MM1 model remembers previous interactions and ensures continuity by referring back to relevant context. Few-Shot Learning with Instruction Tuning The MM1 model leverages few-shot learning techniques, enabling it to learn from a small amount of labeled data. Additionally, it fine-tunes its performance based on specific instructions, adapting to different tasks efficiently. For instance, if provided with only a handful of examples for a new task, the MM1 model can generalize and perform well without extensive training data. Visual Question Answering (VQA) The MM1 model can answer questions related to visual content through Visual Question Answering (VQA). Given an image and a question, it generates accurate and context-aware answers, demonstrating its robust understanding of visual information. For example, when asked, “What is the color of the car in the picture?” the MM1 model can analyze the image and provide an appropriate response. Captioning When presented with an image, the MM1 model can generate descriptive captions. Its ability to capture relevant details and convey them in natural language makes it valuable for image captioning tasks. For instance, if shown a picture of a serene mountain landscape, the MM1 model might generate a caption like, “Snow-capped peaks against a clear blue sky.” For more information, read the paper of Arxiv published by Apple researchers: MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training. Key Components of MM1 Transformer Architecture The transformer architecture serves as the backbone of MM1. Self-Attention Mechanism: Transformers use self-attention to process sequences of data. This mechanism allows them to weigh the importance of different elements within a sequence, capturing context and relationships effectively. Layer Stacking: Multiple layers of self-attention are stacked to create a deep neural network. Each layer refines the representation of input data. Positional Encoding: Transformers incorporate positional information, ensuring they understand the order of elements in a sequence. Multimodal Pre-Training Data MM1 benefits from a diverse training dataset: Image-Text Pairs: These pairs directly connect visual content (images) with corresponding textual descriptions. The model learns to associate the two modalities. Interleaved Documents: Combining images and text coherently allows MM1 to handle multimodal inputs seamlessly. Text-Only Data: Ensuring robust language understanding, even when dealing with text alone. Image Encoder The image encoder is pivotal for MM1’s performance: Feature Extraction: The image encoder processes visual input (images) and extracts relevant features. These features serve as the bridge between the visual and textual modalities. Resolution and Token Count: Design choices related to image resolution and token count significantly impact MM1’s ability to handle visual information. Vision-Language Connector The vision-language connector facilitates communication between textual and visual representations: Cross-Modal Interaction: It enables MM1 to align information from both modalities effectively. Joint Embeddings: The connector generates joint embeddings that capture shared semantics. Ablation Study for MLLMs Building performant Multimodal Large Language Models (MLLMs) is an empirical process that involves carefully exploring various design decisions related to architecture, data, and training procedures. Here, the authors present a detailed ablation study conducted to identify optimal configurations for constructing a high-performing model, referred to as MM1. The ablations are performed along three major axes: MM1 Model Ablations Different pre-trained image encoders are investigated, along with various methods of connecting Large Language Models (LLMs) with these encoders. The architecture exploration encompasses the examination of the image encoder pre-training objective, image resolution, and the design of the vision-language connector. MM1 Model Ablation MM1 Data Ablations Various types of data and their relative mixture weights are considered, including captioned images, interleaved image-text documents, and text-only data. The impact of different data sources on zero-shot and few-shot performance across multiple captioning and Visual Question Answering (VQA) tasks is evaluated. Data Ablation Study for MM1 Training Procedure Ablations The training procedure is explored, including hyperparameters and which parts of the model to train at different stages. Two types of losses are considered: contrastive losses (e.g., CLIP-style models) and reconstructive losses (e.g., AIM), with their effects on downstream performance examined. Empirical Setup A smaller base configuration of the MM1 model is used for ablations, allowing for efficient assessment of model performance. The base configuration includes an Image Encoder (ViT-L/14 model trained with CLIP loss on DFN-5B and VeCap-300M datasets), Vision-Language Connector (C-Abstractor with 144 image tokens), Pre-training Data (mix of captioned images, interleaved image-text documents, and text-only data), and a 1.2B transformer decoder-only Language Model. Zero-shot and few-shot (4- and 8-shot) performance on various captioning and VQA tasks are used as evaluation metrics. MM1 Ablation Study: Key Findings Image resolution, model size, and training data composition are identified as crucial factors affecting model performance. The number of visual tokens and image resolution significantly impact the performance of the Vision-Language Connector, while the type of connector has a minimal effect. Interleaved data is crucial for few-shot and text-only performance, while captioning data enhances zero-shot performance. Text-only data helps improve few-shot and text-only performance, contributing to better language understanding capabilities. Careful mixture of image and text data leads to optimal multimodal performance while retaining strong text performance. Synthetic caption data (VeCap) provides a notable boost in few-shot learning performance. Performance Evaluation of MM1 Models The performance evaluation of MM1 models encompasses several key aspects, including scaling via Mixture-of-Experts (MoE), supervised fine-tuning (SFT) experiments, impact of image resolution, pre-training effects, and qualitative analysis. Scaling via Mixture-of-Experts (MoE) MM1 explores scaling the dense model by incorporating more experts in the Feed-Forward Network (FFN) layers of the language model. Two MoE models are designed: 3B-MoE with 64 experts and 7B-MoE with 32 experts, utilizing top-2 gating and router z-loss terms for training stability. The MoE models demonstrate improved performance over their dense counterparts across various benchmarks, indicating the potential of MoE for further scaling. Supervised Fine-Tuning Experiments Supervised Fine-Tuning (SFT) is performed on top of the pre-trained MM1 models using a diverse set of datasets, including instruction-response pairs, academic task-oriented vision-language datasets, and text-only data. MM1 models exhibit competitive performance across 12 benchmarks, showing particularly strong results on tasks such as VQAv2, TextVQA, ScienceQA, and newer benchmarks like MMMU and MathVista. The models maintain multi-image reasoning capabilities even during SFT, enabling few-shot chain-of-thought reasoning. Impact of Image Resolution Higher image resolution leads to improved performance, supported by methods such as positional embedding interpolation and sub-image decomposition. MM1 achieves a relative performance increase of 15% by supporting an image resolution of 1344×1344 compared to a baseline model with an image resolution of 336 pixels. Pre-Training Effects Large-scale multimodal pre-training significantly contributes to the model's performance improvement over time, showcasing the importance of pre-training data quantity. MM1 demonstrates strong in-context few-shot learning and multi-image reasoning capabilities, indicating the effectiveness of large-scale pre-training for enhancing model capabilities. Qualitative Analysis Qualitative examples provided in the evaluation offer further insights into MM1's capabilities, including single-image and multi-image reasoning, as well as few-shot prompting scenarios. These examples highlight the model's ability to understand and generate contextually relevant responses across various tasks and input modalities. Apple’s Ethical Guidelines for MM1 Privacy and Data Security: Apple places utmost importance on user privacy. MM1 models are designed to respect user data and adhere to strict privacy policies. Any data used for training is anonymized and aggregated. Bias Mitigation: Apple actively works to reduce biases in MM1 models. Rigorous testing and monitoring are conducted to identify and rectify any biases related to gender, race, or other sensitive attributes. Transparency: Apple aims to be transparent about the capabilities and limitations of MM1. Users should have a clear understanding of how the model works and what it can and cannot do. Fairness: MM1 is trained on diverse data, but Apple continues to improve fairness by addressing underrepresented groups and ensuring equitable outcomes. Safety and Harm Avoidance: MM1 is designed to avoid harmful or unsafe behavior. It refrains from generating content that could cause harm, promote violence, or violate ethical norms. Human Oversight: Apple maintains a strong human-in-the-loop approach. MM1 models are continuously monitored, and any problematic outputs are flagged for review. MM1 MLLM: Key Takeaways Multimodal Integration: MM1 combines textual and visual information, achieving impressive performance. Ablation Study Insights: Image encoder matters, connector less so. Data mix is crucial. Scaling Up MM1: Up to 30 billion parameters, strong pre-training metrics, competitive fine-tuning. Ethical Guidelines: Privacy, fairness, safety, and human oversight are priorities.
March 26
10 min
Microsoft MORA: Multi-Agent Video Generation Framework
What is Mora? Mora is a multi-agent framework designed for generalist video generation. Based on OpenAI's Sora, it aims to replicate and expand the range of generalist video generation tasks. Sora, famous for making very realistic and creative scenes from written instructions, set a new standard for creating videos that are up to a minute long and closely match the text descriptions given. Mora distinguishes itself by incorporating several advanced visual AI agents into a cohesive system. This lets it undertake various video generation tasks, including text-to-video generation, text-conditional image-to-video generation, extending generated videos, video-to-video editing, connecting videos, and simulating digital worlds. Mora can mimic Sora’s capabilities using multiple visual agents, significantly contributing to video generation. In this article, you will learn: Mora's innovative multi-agent framework for video generation. The importance of open-source collaboration that Mora enables. Mora's approach to complex video generation tasks and instruction fidelity. About the challenges in video dataset curation and quality enhancement. TL; DR Mora's novel approach uses multiple specialized AI agents, each handling different aspects of the video generation process. This innovation allows various video generation tasks, showcasing adaptability in creating detailed and dynamic video content from textual descriptions. Mora aims to fix the problems with current models like Sora, which is closed-source and does not let anyone else use it or do more research in the field, even though it has amazing text-to-video conversion abilities 📝🎬. Unfortunately, Mora still has problems with dataset quality, video fidelity, and ensuring that outputs align with complicated instructions and people's preferences. These problems show where more work needs to be done in the future. OpenAI Sora’s Closed-Source Nature The closed-source nature of OpenAI's Sora presents a significant challenge to the academic and research communities interested in video generation technologies. Sora's impressive capabilities in generating realistic and detailed videos from text descriptions have set a new standard in the field. Related: New to Sora? Check out our detailed explainer on the architecture, relevance, limitations, and applications of Sora. However, the inability to access its source code or detailed architecture hinders external efforts to replicate or extend its functionalities. This limits researchers from fully understanding or replicating its state-of-the-art performance in video generation. Here are the key challenges highlighted due to Sora's closed-source nature: Inaccessibility to Reverse-Engineer Without access to Sora's source code, algorithms, and detailed methodology, the research community faces substantial obstacles in dissecting and understanding the underlying mechanisms that drive its exceptional performance. This lack of transparency makes it difficult for other researchers to learn from and build upon Sora's advancements, potentially slowing down the pace of innovation in video generation. Extensive Training Datasets Sora's performance is not just the result of sophisticated modeling and algorithms; it also benefits from training on extensive and diverse datasets. But the fact that researchers cannot get their hands on similar datasets makes it very hard to copy or improve Sora's work. High-quality, large-scale video datasets are crucial for training generative models, especially those capable of creating detailed, realistic videos from text descriptions. However, these datasets are often difficult to compile due to copyright issues, the sheer volume of data required, and the need for diverse, representative samples of the real world. Creating, curating, and maintaining high-quality video datasets requires significant resources, including copyright permissions, data storage, and management capabilities. Sora's closed nature worsens these challenges by not providing insights into compiling the datasets, leaving researchers to navigate these obstacles independently. Computational Power Creating and training models like Sora require significant computational resources, often involving large clusters of high-end GPUs or TPUs running for extended periods. Many researchers and institutions cannot afford this much computing power, which makes the gap between open-source projects like Mora and proprietary models like Sora even bigger. Without comparable computational resources, it becomes challenging to undertake the necessary experimentation—with different architectures and hyperparameters—and training regimes required to achieve similar breakthroughs in video generation technology. Learn more about these limitations in the technical paper. Evolution: Text-to-Video Generation Over the years, significant advancements in text-to-video generation technology have occurred, with each approach and architecture uniquely contributing to the field's growth. Here's a summary of these evolutionary stages, as highlighted in the discussion about text-to-video generation in the Mora paper: GANs (Generative Adversarial Networks) Early attempts at video generation leveraged GANs, which consist of two competing networks: a generator that creates images or videos that aim to be indistinguishable from real ones, and a discriminator that tries to differentiate between the real and generated outputs. Despite their success in image generation, GANs faced challenges in video generation due to the added complexity of temporal coherence and higher-dimensional data. Generative Video Models Moving beyond GANs, the field saw the development of generative video models designed to produce dynamic sequences. Generating realistic videos frame-by-frame and maintaining temporal consistency is a challenge, unlike in static image generation. Auto-Regressive Transformers Auto-regressive transformers were a big step forward because they could generate video sequences frame-by-frame. These models predicted each new frame based on the previously generated frames, introducing a sequential element that mirrors the temporal progression of videos. But this approach often struggled with long-term coherence over longer sequences. Large-Scale Diffusion Models Diffusion models, known for their capacity to generate high-quality images, were extended to video generation. These models gradually refine a random noise distribution toward a coherent output. They apply this iterative denoising process to the temporal domain of videos. Related: Read our guide on HuggingFace’s Dual-Stream Diffusion Net for Text-to-Video Generation. Image Diffusion U-Net Adapting the U-Net architecture for image diffusion models to video content was critical. This approach extended the principles of image generation to videos, using a U-Net that operates over sequences of frames to maintain spatial and temporal coherence. 3D U-Net Structure The change to a 3D U-Net structure allowed for more nuance in handling video data, considering the extra temporal dimension. This change also made it easier to model time-dependent changes, improving how we generate coherent and dynamic video content. Latent Diffusion Models (LDMs) LDMs generate content in a latent space rather than directly in pixel space. This approach reduces computational costs and allows for more efficient handling of high-dimensional video data. LDMs have shown that they can better capture the complex dynamics of video content. Diffusion Transformers Diffusion transformers (DiT) combine the strengths of transformers in handling sequential data with the generative capabilities of diffusion models. This results in high-quality video outputs that are visually compelling and temporally consistent. Useful: Stable Diffusion 3 is an example of a multimodal diffusion transformer model that generates high-quality images and videos from text. Check out our explainer on how it works. AI Agents: Advanced Collaborative Multi-agent Structures The paper highlights the critical role of collaborative, multi-agent structures in developing Mora. It emphasizes their efficacy in handling multimodal tasks and improving video generation capabilities. Here's a concise overview based on the paper's discussion on AI Agents and their collaborative frameworks: Multimodal Tasks Advanced collaborative multi-agent structures address multimodal tasks involving processing and generating complex data across different modes, such as text, images, and videos. These structures help integrate various AI agents, each specialized in handling specific aspects of the video generation process, from understanding textual prompts to creating visually coherent sequences. Cooperative Agent Framework (Role-Playing) The cooperative agent framework, characterized by role-playing, is central to the operation of these multi-agent structures. Each agent is assigned a unique role or function in this framework, such as prompt enhancement, image generation, or video editing. By defining these roles, the framework ensures that an agent with the best skills for each task is in charge of that step in the video generation process, increasing overall efficiency and output quality. Multi-Agent Collaboration Strategy The multi-agent collaboration strategy emphasizes the orchestrated interaction between agents to achieve a common goal. In Mora, this strategy involves the sequential and sometimes parallel processing of tasks by various agents. For instance, one agent might enhance an initial text prompt, convert it into another image, and finally transform it into a video sequence by yet another. This collaborative approach allows for the flexible and dynamic generation of video content that aligns with user prompts. AutoGen (Generic Programming Framework) A notable example of multi-agent collaboration in practice is AutoGen. This generic programming framework is designed to automate the assembly and coordination of multiple AI agents for a wide range of applications. Within the context of video generation, AutoGen can streamline the configuration of agents according to the specific requirements of each video generation task to generate complex video content from textual or image-based prompts. Mora drone to butterfly flythrough shot. | Image Source. Role of an AI Agent The paper outlines the architecture involving multiple AI agents, each serving a specific role in the video generation process. Here's a closer look at the role of each AI agent within the framework: Illustration of how to use Mora to conduct video-related tasks Prompt Selection and Generation Agent This agent is tasked with processing and optimizing textual prompts for other agents to process them further. Here are the key techniques used for Mora: GPT-4: This agent uses the generative capabilities of GPT-4 to generate high-quality prompts that are detailed and rich in context. Prompt Selection: This involves selecting or enhancing textual prompts to ensure they are optimally prepared for the subsequent video generation process. This step is crucial for setting the stage for generating images and videos that closely align with the user's intent. Good Read: Interested in GPT-4 Vision alternatives? Check out our blog post. Text-to-Image Generation Agent This agent uses a retrained large text-to-image model to convert the prompts into initial images. The retraining process ensures the model is finely tuned to produce high-quality images, laying a strong foundation for the video generation process. Image-to-Image Generation Agent This agent specializes in image-to-image generation, taking initial images and editing them based on new prompts or instructions. This ability allows for a high degree of customization and improvement in video creation. Image-to-Video Generation Agent This agent transforms static images into dynamic video sequences, extending the visual narrative by generating coherent frames. Here are the core techniques and models: Core Components: It incorporates two pre-trained models: GPT-3 for understanding and generating text-based instructions, and Stable Diffusion for translating these instructions into visual content. Prompt-to-Prompt Technique: The prompt-to-prompt technique guides the transformation from an initial image to a series of images that form a video sequence. Classifier-Free Guidance: Classifier-free guidance is used to improve the fidelity of generated videos to the textual prompts so that the videos remain true to the users' vision. Text-to-Video Generation Agent: This role is pivotal in transforming static images into dynamic videos that capture the essence of the provided descriptions. Stable Video Diffusion (SVD) and Hierarchical Training Strategy: A model specifically trained to understand and generate video content, using a hierarchical training strategy to improve the quality and coherence of the generated videos. Video Connection Agent This agent creates seamless transitions between two distinct video sequences for a coherent narrative flow. Here are the key techniques used: Pre-Trained Diffusion-Based T2V Model: This model uses a pre-trained diffusion-based model specialized in text-to-video (T2V) tasks to connect separate video clips into a cohesive narrative. Text-Based Control: This method uses textual descriptions to guide the generation of transition videos that seamlessly connect disparate video clips, ensuring logical progression and thematic consistency. Image-to-Video Animation and Autoregressive Video Prediction: These capabilities allow the agent to animate still images into video sequences, predict and generate future video frames based on previous sequences, and create extended and coherent video narratives. Mora’s Video Generation Process Mora's video-generation method is a complex, multi-step process that uses the unique capabilities of specialized AI agents within its framework. This process allows Mora to tackle various video generation tasks, from creating videos from text descriptions to editing and connecting existing videos. Here's an overview of how Mora handles each task: Mora’s video generation process. Text-to-Video Generation This task begins with a detailed textual prompt from the user. Then, the Text-to-Image Generation Agent converts the prompts into initial static images. These images serve as the basis for the Image-to-Video Generation Agent, which creates dynamic sequences that encapsulate the essence of the original text and produce a coherent video narrative. Text-Conditional Image-to-Video Generation This task combines textual prompts with a specific starting image. Mora first improves the input with the Prompt Selection and Generation Agent, ensuring that the text and image are optimally prepared for video generation. Then, the Image-to-Video Generation Agent takes over, generating a video that evolves from the initial image and aligns with the textual description. Extend Generated Videos To extend an existing video, Mora uses the final frame of the input video as a launchpad. The Image-to-Video Generation Agent crafts additional sequences that logically continue the narrative from the last frame, extending the video while maintaining narrative and visual continuity. Video-to-Video Editing In this task, Mora edits existing videos based on new textual prompts. The Image-to-Image Generation Agent first edits the video's initial frame according to the new instructions. Then, the Image-to-Video Generation Agent generates a new video sequence from the edited frame, adding the desired changes to the video content. Connect Videos Connecting two videos involves creating a transition between them. Mora uses the Video Connection Agent, which analyzes the first video's final frame and the second's initial frame. It then generates a transition video that smoothly links the two segments into a cohesive narrative flow. Simulating Digital Worlds Mora generates video sequences in this task that simulate digital or virtual environments. The process involves appending specific style cues (e.g., "in digital world style") to the textual prompt, guiding the Image-to-Video Generation Agent to create a sequence reflecting the aesthetics of a digital realm. This can involve stylistically transforming real-world images into digital representations or generating new content within the specified digital style. See Also: Read our explainer on Google’s Video Gaming Companion: Scalable Instructable Multiworld Agent [SIMA]. Mora: Experimental Setup As detailed in the paper, the experimental setup for evaluating Mora is comprehensive and methodically designed to assess the framework's performance across various dimensions of video generation. Here's a breakdown of the setup: Baseline The baseline for comparison includes existing open-sourced models that showcase competitive performance in video generation tasks. These models include Videocrafter, Show-1, Pika, Gen-2, ModelScope, LaVie-Interpolation, LaVie, and CogVideo. These models are a reference point for evaluating Mora's advancements and position relative to the current state-of-the-art video generation. Basic Metrics The evaluation framework comprises several metrics to quantify Mora's performance across different dimensions of video quality and condition consistency: Video Quality Measurement Object Consistency: Measures the stability of object appearances across video frames. Background Consistency: Assesses the uniformity of the background throughout the video. Motion Smoothness: Evaluates the fluidity of motion within the video. Aesthetic Score: Gauges the artistic and visual appeal of the video. Dynamic Degree: Quantifies the video's dynamic action or movement level. Imaging Quality: Assesses the overall visual quality of the video, including clarity and resolution. Video Condition Consistency Metric Temporal Style: Measures how consistently the video reflects the temporal aspects (e.g., pacing, progression) described in the textual prompt. Appearance Style: Evaluates the adherence of the video's visual style to the descriptions provided in the prompt, ensuring that the generated content matches the intended appearance. Self-Defined Metrics Video-Text Integration (VideoTI): Measures the model’s fidelity to textual instructions by comparing text representations of input images and generated videos. Temporal Consistency (TCON): Evaluates the coherence between an original video and its extended version, providing a metric for assessing the integrity of extended video content. Temporal Coherence (Tmean): Quantifies the correlation between the intermediate generated and input videos, measuring overall temporal coherence. Video Length: This parameter quantifies the duration of the generated video content, indicating the model's capacity for producing videos of varying lengths. Implementation Details The experiments use high-performance hardware, specifically TESLA A100 GPUs with substantial VRAM. This setup ensures that Mora and the baseline models are evaluated under conditions allowing them to fully express their video generation capabilities. The choice of hardware reflects the computational intensity of training and evaluating state-of-the-art video generation models. Mora video generation - Fish underwater flythrough Limitations of Mora The paper outlines several limitations of the Mora framework. Here's a summary of these key points: Curating High-Quality Video Datasets Access to high-quality video datasets is a major challenge for training advanced video generation models like Mora. Copyright restrictions and the sheer volume of data required make it difficult to curate diverse and representative datasets that can train models capable of generating realistic and varied video content. Read Also: The Full Guide to Video Annotation for Computer Vision. Quality and Length Gaps While Mora demonstrates impressive capabilities, it has a noticeable gap in quality and maximum video length compared to state-of-the-art models like Sora. This limitation is particularly evident in tasks requiring the generation of longer videos, where maintaining visual quality and coherence becomes increasingly challenging. Simulating videos in Mora vs in Sora. Instruction Following Capability Mora sometimes struggles to precisely follow complex or detailed instructions, especially when generating videos that require specific actions, movements, or directionality. This limitation suggests that further improvement in understanding and interpreting textual prompts is needed. Human Visual Preference Alignment The experimental results may not always align with human visual preferences, particularly in scenarios requiring the generation of realistic human movements or the seamless connection of video segments. This misalignment highlights the need to incorporate a more nuanced understanding of physical laws and human dynamics into the video-generation process. Mora Vs. Sora: Feature Comparisons The paper compares Mora and OpenAI's Sora across various video generation tasks. Here's a detailed feature comparison based on their capabilities in different aspects of video generation: Check out the project repository on GitHub. Mora Multi-Agent Framework: Key Takeaways The paper "Mora: Enabling Generalist Video Generation via a Multi-Agent Framework" describes Mora, a new framework that advances video technology. Using a multi-agent approach, Mora is flexible and adaptable across various video generation tasks, from creating detailed scenes to simulating complex digital worlds. Because it is open source, it encourages collaboration, which leads to new ideas, and lets the wider research community add to and improve its features. Even though Mora has some good qualities, it needs high-quality video datasets, video quality, length gaps, trouble following complicated instructions correctly, and trouble matching outputs to how people like to see things. Finding solutions to these problems is necessary to make Mora work better and be used in more situations. Continuing to improve and develop Mora could change how we make video content so it is easier for creators and viewers to access and have an impact.
March 26
8 min
Best Image Annotation Tools for Computer Vision [Updated 2024]
Guide to the most popular image annotation tools that you need to know about in 2024. Compare the features and pricing, and choose the best image annotation tool for your use case. It’s 2024—annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we put together a list of the most popular image labeling tools out there. Whether you are: A computer vision team building unmanned drones with your own in-house annotation tool. A team of data scientists working on an autonomous driving project looking for large-scale labeling services. Or a data operations team working in healthcare looking for the right platform for your radiologists to accurately label CT scans. This guide will help you compare the top AI annotation tools and find the right one for you. We will compare each based on key factors - including image annotation service, support for different data types and use cases, QA/QC capabilities, security and data privacy, integration with the machine learning pipeline, and customer support. But first, let's explore the process of selecting an image annotation tool from the available providers. Choosing the right image annotation tool is a critical decision that can significantly impact the quality and efficiency of the annotation process. To make an informed choice, it's essential to consider several factors and evaluate the suitability of an image annotation tool for specific needs. Evaluating Image Annotation Tools for Computer Vision Projects Selecting the perfect image annotation tool is like choosing the perfect brush for your painting. Different projects require specific annotation needs that dictate how downstream components. When evaluating an annotation tool that fits your project specifications, there are a few key factors you have to consider. In this section, we will explore those key factors and practical considerations to help you navigate the selection process and find the most fitting AI annotation tool for your computer vision applications. Annotation Types: An effective labeling tool should support various annotation types, such as bounding boxes (ideal for object localization), polygons (useful for detailed object outlines), keypoints (for pose estimation), and semantic segmentation (for scene understanding). The tool must be adaptable to different annotation requirements, allowing users to annotate images with precision and specificity based on the task at hand. User Interface (UI) and User Experience (UX): The user interface plays a crucial role in the efficiency and accuracy of the annotation process. A good annotation tool should have an intuitive interface that is easy to navigate, reducing the learning curve for users. Clear instructions, user-friendly controls, and efficient workflows contribute to a smoother annotation experience. Scalability: Consider the tool's ability to scale with the growing volume of data. A tool that efficiently handles large datasets and multiple annotators is crucial for projects with evolving requirements. Automation and AI Integration: Look for image labeling tools that offer automation features, such as automatic annotation tools or features, to accelerate the annotation process. Integration with artificial intelligence (AI) algorithms can further enhance efficiency by automating repetitive tasks, reducing manual effort, and improving annotation accuracy. Collaboration and Workflow Management: Assess the data annotation tool's collaboration features, including version control, user roles, and workflow management. Collaboration tools are essential for teams working on complex annotation projects. Data Security and Privacy: Ensure that the tool adheres to data security and privacy standards like GDPR. Evaluate encryption methods, access controls, and policies regarding the handling of sensitive data. Pricing: Consider various pricing models, such as per-user, per-project, or subscription models. Also factor in scalability costs, and potential additional fees, ensuring transparency in the pricing structure. Once you've identified which factors are most important for you to evaluate image annotating tools, the next step is understanding how to assess their suitability for your specific use case. Most Popular Image Annotation Tools Let's compare the features offered by the best image annotation companies such as Encord, Scale AI, Label Studio, SuperAnnotate, CVAT, and Amazon SageMaker Ground Truth, and understand how they assist in annotating images. This article discusses the top 17 image annotation tools in 2024 to help you choose the right image annotation software for your use case. Encord Scale CVAT Label Studio Labelbox Playment Appen Dataloop SuperAnnotate V7 Labs Hive COCO Annotator Make Sense VGG Image Annotator LabelMe Amazon SageMaker Ground Truth VOTT Encord Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management. Key Features Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling. AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA. Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines. Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality. Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale. Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors. Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for Commercial teams: Teams translating from an in-house solution or open-source tool that require a scalable annotation workflow with a robust, secure, and collaborative enterprise-grade platform. Complex or unique use case: For teams that require advanced annotation tool and functionality. It includes, complex nested ontologies or rendering native DICOM formats. Pricing Simple per-user pricing – no need to track annotation hours, label consumption or data usage. Curious? Try it out Scale Scale AI, now Scale, is a data and labeling services platform that supports computer vision use cases but specializes in RLHF, user experience optimization, large language models, and synthetic data. Scale AI's Image Annotation Tool Key Features Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases. Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Best for Teams Looking for a Labeling Tool: Scale is a very popular option for data labeling services. Teams Looking for Annotation Tools for Autonomous Vehicle Vision: Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases. Teams Looking for Medical Imaging Annotation Tools: Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their data annotators on the platform. Pricing On a per-image basis CVAT (Computer Vision Annotation Tool) CVAT is an open source image annotation tool that is a web-based annotation toolkit, built by Intel. For image labeling, CVAT supports four types of annotations: points, polygons, bounding boxes, and polylines, as well as a subset of computer vision tasks: image segmentation, object detection, and image classification. In 2022, CVAT’s data, content, and GitHub repository were migrated over to OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also be utilized to annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Label Editor Key Features Open-source: Easy and free to get started labeling images. Manual Annotation Tools: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on various operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT offers support for various data formats including JSON, COCO, and XML-based like Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Best for Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset). Not preferable for commercial teams as it lacks scalability, collaborative features, and robust security. Pricing Free 💡 More insights on image labeling with CVAT: For a team looking for free image annotation tools, CVAT is one of the most popular open-source tools in the space, with over 1 million downloads since 2021. Other popular free image annotation alternatives to CVAT are 3D Slicer, Labelimg, VoTT (Visual Object Tagging Tool - developed by Microsoft), VIA (VGG Image Annotator), LabelMe, and Label Studio. If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are usually not available in open-source tools. Further reading: Overview of open source annotation tools for computer vision Complete guide to image annotation for computer vision Label Studio Label Studio is another popular open source data labeling platform. It provides a versatile platform for annotating various data types, including images, text, audio, and video. Label Studio supports collaborative labeling, custom labeling interfaces, and integration with machine learning pipelines for data annotation tasks. Label Studio Image Annotation Tool Key Features Customizable Labeling Interfaces: Flexible configuration for tailored annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project sharing capabilities for seamless collaboration among annotators. Extensible: Easily connect to cloud object storage and label data there directly Export Formats: Label Studio supports multiple data formats including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks. Best for Data scientists, machine learning engineers, and researchers or teams requiring versatile data labeling for images. Not suitable for teams with limited technical expertise or resources for managing an open source tool Price Free with enterprise plan available Labelbox Labelbox is a US-based data annotation platform founded in 2017. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services. Labelbox Image Editor Key Features Data Management: QA workflows and data annotator performance tracking. Customizable Labeling Interface: 3rd party labeling services through Labelbox Boost. Automation: Integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Type: Support for multiple data types beyond images, especially text. Best for Teams looking for a platform to quickly annotate documents and text. Teams carrying out annotation projects that are use-case specific. As generalist tools, platforms like Labelbox are great at handling a broad variety of data types. If you’re working on a unique use-case-specific annotation project (like scans in DICOM formats or high-resolution images that require pixel-perfect annotations), other commercial AI labeling tools will be a better fit: check out our blog exploring Best DICOM Labeling Tools. Pricing Varies based on the volume of data, percent of the total volume needing to be labeled, number of seats, number of projects, and percent of data used in model training. For larger commercial teams, this pricing may get expensive as your project scales. Playment Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021 and provides computer vision teams with training data for various use cases, supported by manual labelers and a machine learning platform. Playment Image Annotation Tool Key Features Data Labeling Services: Provides high-quality data labeling services for various data types including images, videos, text, and sensor data. Support: Global workforces of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: Speech recognition training platform (handles all data types across 500+ languages and dialects). Best for Teams looking for a fully managed solution who do not need visibility into the process. Pricing Enterprise plan Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market. The company offers data labeling services for a wide range of industries and in 2019, acquired Figure Eight to build out its software capabilities and help businesses also train and improve their computer vision models. Appen Image Annotation Tool Key Features Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation). Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation. Natural Language Processing: Supports natural language processing tasks such as sentiment analysis, entity recognition, and text classification. Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation. Best for Teams looking for image data sourcing and collection alongside annotation services. Pricing Enterprise plan Dataloop Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models. Dataloop Image Annotation Tool Key Features Data Annotation: Features for image annotation tasks, including classification, detection, and semantic segmentation. Video Annotation Tool: Support for video annotations. Collaboration Tool: Features for real-time collaboration among annotators, project sharing, and version control for efficient teamwork. Data Management: Offers data management capabilities including data versioning, tracking, and organization for streamlined workflows. Best for Teams looking for a generalist annotation tool for various data annotation needs. Teams carrying out specific image and video annotation projects that are use-case specific. As generalist tools, platforms like Dataloop are built to support a wide variety of simple use cases, so other commercial platforms are a better fit if you’re trying to label use-case-specific annotation projects (like high-resolution images that require pixel-perfect annotations in satellite imaging or DICOM files for medical teams). Pricing Free trial and an enterprise plan. SuperAnnotate SuperAnnotate provides enterprise solutions for image and video annotation, catering primarily to the needs of the computer vision community. It provides powerful annotation tools and features tailored for machine learning and AI applications, offering efficient labeling solutions to enhance model training and accuracy. SuperAnnotate - Image Annotation Tool Key Features Multi-Data Type Support: Versatile annotation tool for image, video, text, and audio. AI Assistance: Integrates AI-assisted annotation to accelerate the annotation process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks according to specific project requirements. Integration: Seamlessly integrates with machine learning pipelines and workflows for efficient model training and deployment. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. Best for Larger teams working on various machine learning solutions looking for a versatile annotation tool. Pricing Free for early stage startups and academics for team size up to 3. Enterprise plan V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate training data, support the human-in-the-loop processes, and also connect with annotation services. V7 offers annotation of a wide range of data types alongside image annotation tooling, including documents and videos. V7 Labs Image Annotation Tool Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Labeling Services: Provides labeling services for images and videos. AI Assistance: Model-assisted annotation of multiple annotation types (segmentation, detection, and more). Best for Students or teams looking for a generalist platform to easily annotate different data types in one place (like documents, images, and short videos). Limited functionalities for use-case specific annotations. Pricing Various options, including academic, business, and pro. Hive Hive was founded in 2013 and provides cloud-based AI solutions for companies wanting to label content across a wide range of data types, including images, video, audio, text, and more. Hive Image Annotation Tool Key Features Image Annotation Tool: Offers annotation tools and workflows for labeling images along with support for unique image annotation use cases (ad targeting, semi-automated logo detection). Ease of Access: Flexible access to model predictions with a single API call. Integration: Seamlessly integrates with machine learning pipelines and workflows for AI model training and deployment. Best for Teams labeling images and other data types for the purpose of content moderation. Pricing Enterprise plan COCO Annotator COCO Annotator is a web-based image annotation tool, crafted by Justin Brooks under the MIT license. Specifically designed to streamline the process of labeling images for object detection, localization, and keypoints detection models, this tool offers a range of features that cater to the diverse needs of machine learning practitioners and researchers. COCO Annotator - Image Annotation Tool Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: To facilitate large-scale object detection, the tool exports and stores annotations in the COCO format. Automations: The tool makes annotating an image easier by incorporating semi-trained models. Additionally, it provides access to advanced selection tools, including the MaskRCNN, Magic Wand and DEXTR. Best For ML Research Teams: COCO Annotator is a good choice for ML researchers, preferable for image annotation for tasks like object detection and keypoints detection. Price Free Make Sense Make Sense AI is a user-friendly and open-source annotation tool, available under the GPLv3 license. Accessible through a web browser without the need for advanced installations, this tool simplifies the annotation process for various image types. Make Sense - Image Annotation Tool Key Features Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development. Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices. Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML like Pascal VOC, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms and seamless integration into various workflows. Best For Small teams seeking an efficient solution to annotate an image. Price Free VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool crafted by the Visual Geometry Group (VGG) for the manual annotation of both image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of both academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VGG Image Annotator - Image Annotation Tool Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool, utilizing HTML, Javascript, and CSS without external libraries, enabling offline usage in modern web browsers without setup or installation. Offline Capability: The tool is designed to be used offline, providing a full application experience within a single HTML file of size less than 200 KB. Multi-User Collaboration: Facilitates collaboration among multiple annotators with features such as project sharing, real-time annotation, and version control. Best For VGG Image Annotator (VIA) is ideal for individuals and small teams involved in projects for academic researchers. Price Free LabelMe LabelMe is an open-source web-based tool developed by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images. LabelMe Image Annotation Tool Key Features Web-Based: Accessible through a web-based interface, allowing for annotation tasks to be performed in any modern web browser without requiring software installation. Customizable Interface: Provides a customizable annotation interface with options to adjust settings, colors, and layout preferences to suit specific project requirements. Best for Academic and research purposes Pricing Free Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS). It offers a platform for efficiently labeling large datasets to train machine learning models. Ground Truth supports various annotation tasks, including image classification, object detection, semantic segmentation, and more. Amazon SageMaker Ground Truth - Image Annotation Tool Key Features Managed Service: Fully managed by AWS, eliminating the need for infrastructure setup and management. Human-in-the-Loop Labeling: Harnesses the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Integration with Amazon SageMaker: Seamlessly integrates with Amazon SageMaker for model training and deployment, providing a streamlined end-to-end machine learning workflow. Best for Teams requiring large-scale data labeling. Pricing Varies based on labeling task and type of data. VOTT VOTT or Visual Object Tagging Tool is an open-source tool developed by Microsoft for annotating images and videos to create training datasets for computer vision models. VOTT provides an intuitive interface for drawing bounding boxes around objects of interest and labeling them with corresponding class names. VOTT Image Annotation Tool Key Features Versatile Annotation Tool: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Video Annotation: Enables annotation of videos frame by frame, with support for object tracking and interpolation to streamline the annotation process. Multi-Platform Compatibility: Works across various operating systems such as Windows, Linux, and macOS, ensuring flexibility for users. Best for Teams requiring lightweight and customizable annotation tool for object detection. Pricing Free Image Annotation Tool: Key Takeaways There you have it! The 17 Best Image Annotation Tools for computer vision in 2024. For further reading, you might also want to check out a few 2024 honorable mentions, both paid and free annotation tools: Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature. Labelimg - Labelimg is an open source multi-modal data annotation tool now part of Label Studio. MarkUp - MarkUp image is a free web annotation tool to annotate an image or a PDF.
March 26
10 min
Announcing Auto-Segmentation Tracking For Video
In computer vision, where accurate training data is the lifeblood of successful models, video annotation plays an important role. However, annotating each frame individually is time-consuming and prone to inconsistencies. Nearby frames often exhibit visual similarities, and annotations made on one frame can be extrapolated to others. Enter automated polygon and bitmask tracking! Automated segmentation tracking significantly reduces annotation time, while simultaneously improving accuracy - gone are the days of tediously labeling every frame in a video. Polygon and Bitmask tracking provides the tooling required to build labeled training data at scale and at speed. Polygon tracking meticulously outlines objects with a series of interconnected vertices, offering precision and flexibility unparalleled in video annotation. Conversely, Bitmask tracking simplifies the annotation process by representing object masks as binary images, streamlining efficiency without compromising clarity. Join us as we explore these techniques that are not just enhancing the process of video annotation, but also paving the way for more accurate and efficient machine learning models. 🚀 Understanding Polygon and Bitmask Tracking Polygon Tracking A polygon is a geometric shape defined by a closed loop of straight-line segments. It can have three or more sides, forming a boundary around an area. In video annotation, polygons are used to outline objects of interest within frames. By connecting a series of vertices, we create a polygon that encapsulates the object’s shape. Advantages of Polygon-Based Tracking Accurate Boundary Representation: Polygons provide a precise representation of an object’s boundary. Unlike bounding boxes (which are rectangular and may not align perfectly with irregular shapes), polygons can closely follow the contours of complex objects. Flexibility: Polygons are versatile. They can adapt to various object shapes, including non-rectangular ones. Whether you’re tracking a car, a person, or an animal, polygons allow for flexibility in annotation. Use Cases of Polygon Tracking Object Segmentation: When segmenting objects from the background, polygons excel. For instance, in medical imaging, they help delineate tumors or organs. Motion Analysis: Tracking moving objects often involves polygon-based annotation. Analyzing the trajectory of a soccer ball during a match or monitoring pedestrian movement in surveillance videos are examples. Bitmask Tracking A bitmask is a binary image where each pixel corresponds to a specific object label. Instead of outlining the object’s boundary, bitmasks assign a unique value (usually an integer) to each pixel within the object region. These values act as identifiers, allowing pixel-level annotation. Advantages of Bitmask-Based Tracking Bitmasks enable precise delineation at the pixel level. By assigning values to individual pixels, we achieve accurate object boundaries. This is especially useful when dealing with intricate shapes or fine details. Use Cases of Bitmask Tracking Semantic Segmentation: In semantic segmentation tasks, where the goal is to classify each pixel into predefined classes (e.g., road, sky, trees), bitmasks play a vital role. They provide ground truth labels for training deep learning models. Instance Segmentation: For scenarios where multiple instances of the same object class appear in a frame (e.g., identifying individual cars in a traffic scene), bitmask tracking ensures each instance is uniquely labeled. Temporal Consistency Maintaining temporal consistency when annotating objects in a video is crucial. This means that the annotations for an object should be consistent from one frame to the next. Inconsistent annotations can lead to inaccurate results when the annotated data is used for training machine learning models. Temporal smoothing and interpolation techniques can be used to improve the consistency of the tracking. Temporal smoothing involves averaging the annotations over several frames to reduce the impact of any sudden changes. Interpolation, on the other hand, involves estimating the annotations for missing frames based on the annotations of surrounding frames. Both these techniques can greatly improve the quality and consistency Read the documentation, to know how to use interpolation in your annotation. Applications of Polygon and Bitmask Tracking Object Detection and Tracking With polygon tracking, objects of any shape can be accurately annotated, making it particularly useful for tracking objects that have irregular shapes or change shape over time. Bitmask tracking takes this a step further by marking each individual pixel, capturing even the smallest details of the object. This level of precision is crucial for detecting and tracking objects accurately within a video. Semantic Segmentation In semantic segmentation, the goal is to classify each pixel in the image to a particular class, making it a highly detailed task. Bitmask tracking, with its ability to mark each individual pixel, is perfectly suited for this task. It allows for the creation of highly accurate masks that can be used to train models for semantic segmentation. Polygon tracking can also be used for semantic segmentation, especially in scenarios where the objects being segmented have clear, defined boundaries. Interactive Video Editing Interactive video editing is a process where users can manipulate and modify video content. This involves tasks such as object removal, color grading, and adding special effects. Polygon and bitmask tracking can greatly enhance the process of interactive video editing. With these techniques, objects within the video can be accurately tracked and annotated, making it easier to apply edits consistently across multiple frames. This can lead to more seamless and high-quality edits, improving the overall video editing process. Semantic Context and Automation Semantic Context Scene Understanding: When placing polygons or bitmasks for video annotation, it’s crucial to consider the context of the scene. The semantics of the scene can guide accurate annotations. For instance, understanding the environment, the objects present, and their spatial relationships can help in placing more accurate and meaningful annotations. Object Relationships: The way objects interact within a scene significantly affects annotation choices. Interactions such as occlusion (where one object partially or fully hides another) and containment (where one object is inside another) need to be considered. Understanding these relationships can lead to more accurate and contextually relevant annotations. Automated Annotation Tool AI Assitance: With the advancement of machine learning models, we now have the capability to propose initial annotations automatically. These AI tools can significantly reduce the manual effort required in the annotation process. They can quickly analyze a video frame and suggest potential annotations based on learned patterns and features. Human Refinement: While AI tools can propose initial annotations, human annotators are still needed to refine these automated results for precision. Annotators can correct any inaccuracies and add nuances that the AI might have missed. This combination of AI assistance and human refinement leads to a more efficient and accurate video annotation process. Read the blog The Full Guide to Automated Data Annotation for more information. Real-World Applications Polygon and Bitmask tracking, along with the concepts of semantic context and automation, have a wide range of real-world applications. Here are a few key areas where they are making a significant impact: Medical Imaging: In medical imaging, precise annotation can mean the difference between a correct and incorrect diagnosis. These techniques allow for highly accurate segmentation of medical images, which can aid in identifying and diagnosing a wide range of medical conditions. Autonomous Vehicles: Polygon and Bitmask tracking allow these vehicles to understand their environment in great detail, helping them make better driving decisions. Video Surveillance: In video surveillance, tracking objects accurately over time is key to identifying potential security threats. These techniques can improve the accuracy and efficiency of video surveillance systems, making our environments safer. These are just a few examples of the many possible applications of Polygon and Bitmask tracking. As these techniques continue to evolve, they are set to revolutionize numerous industries and fields. In summary, Polygon and Bitmask tracking are transforming video annotation, paving the way for more precise machine learning models. As we continue to innovate in this space, we’re excited to announce that Encord will be releasing new features soon. Stay tuned for these updates and join us in exploring the future of computer vision with Encord. 🚀
March 22
5 min
Top 12 Dimensionality Reduction Techniques for Machine Learning
Dimensionality reduction is a fundamental technique in machine learning (ML) that simplifies datasets by reducing the number of input variables or features. This simplification is crucial for enhancing computational efficiency and model performance, especially as datasets grow in size and complexity. High-dimensional datasets, often comprising hundreds or thousands of features, introduce the "curse of dimensionality." This effect slows down algorithms by making data scarceness (sparsity) and computing needs grow exponentially. Dimensionality reduction changes the data into a simpler, lower-dimensional space that is easier to work with while keeping its main features. This makes computation easier and lowers the risk of overfitting. This strategy is increasingly indispensable in the era of big data, where managing vast volumes of information is a common challenge. This article provides insight into various approaches, from classical methods like principal component analysis (PCA) and linear discriminant analysis (LDA) to advanced techniques such as manifold learning and autoencoders. Each technique has benefits and works best with certain data types and ML problems. This shows how flexible and different dimensionality reduction methods are for getting accurate and efficient model performance when dealing with high-dimensional data. Here are the Twelve (12) techniques you will learn in this article: Manifold Learning (t-SNE, UMAP) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Sequential Non-negative Matrix Factorization (NMF) Linear Discriminant Analysis (LDA) Generalized Discriminant Analysis (GDA) Missing Values Ratio (MVR): Threshold Setting Low Variance Filter High Correlation Filter Forward Feature Construction Backward Feature Elimination Autoencoders Classification of Dimensionality Reduction Techniques Dimensionality reduction techniques preserve important data, make it easier to use in other situations, and speed up learning. They do this using two steps: feature selection, which preserves the most important variables, and feature projection, which creates new variables by combining the original ones in a big way. Feature Selection Techniques Techniques classified under this category can identify and retain the most relevant features for model training. This approach helps reduce complexity and improve interpretability without significantly compromising accuracy. They are divided into: Embedded Methods: These integrate feature selection within model training, such as LASSO (L1) regularization, which reduces feature count by applying penalties to model parameters and feature importance scores from Random Forests. Filters: These use statistical measures to select features independently of machine learning models, including low-variance filters and correlation-based selection methods. More sophisticated filters involve Pearson’s correlation and Chi-Squared tests to assess the relationship between each feature and the target variable. Wrappers: These assess different feature subsets to find the most effective combination, though they are computationally more demanding. Feature Projection Techniques Feature projection transforms the data into a lower-dimensional space, maintaining its essential structures while reducing complexity. Key methods include: Manifold Learning (t-SNE, UMAP). Principal Component Analysis (PCA). Kernel PCA (K-PCA). Linear Discriminant Analysis (LDA). Quadratic Discriminant Analysis (QDA). Generalized Discriminant Analysis (GDA). 1. Manifold Learning Manifold learning, a subset of non-linear dimensionality reduction techniques, is designed to uncover the intricate structure of high-dimensional data by projecting it into a lower-dimensional space. Understanding Manifold Learning At the heart of Manifold Learning is that while data may exist in a high-dimensional space, the intrinsic dimensionality—representing the true degrees of freedom within the data—is often much lower. For example, images of faces, despite being composed of thousands of pixels (high-dimensional data points), might be effectively described with far fewer dimensions, such as the angles and distances between key facial features. Core Techniques and Algorithms t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is powerful for visualizing high-dimensional data in two or three dimensions. It converts similarities between data points to joint probabilities and minimizes the divergence between them in different spaces, excelling in revealing clusters within data. Uniform Manifold Approximation and Projection (UMAP): UMAP is a relatively recent technique that balances the preservation of local and global data structures for superior speed and scalability. It's computationally efficient and has gained popularity for its ability to handle large datasets and complex topologies. Isomap (Isometric Mapping): Isomap extends classical Multidimensional Scaling (MDS) by incorporating geodesic distances among points. It's particularly effective for datasets where the manifold (geometric surface) is roughly isometric to a Euclidean space, allowing global properties to be preserved. Locally Linear Embedding (LLE): LLE reconstructs high-dimensional data points from their nearest neighbors, assuming the manifold is locally linear. By preserving local relationships, LLE can unfold twisted or folded manifolds. t-SNE and UMAP are two of the most commonly applied dimensionality reduction techniques. At Encord, we use UMAP to generate the 2D embedding plots in Encord Active. 2. Principal Component Analysis (PCA) The Principal Component Analysis (PCA) algorithm is a method used to reduce the dimensionality of a dataset while preserving as much information (variance) as possible. As a linear reduction method, PCA transforms a complex dataset with many variables into a simpler one that retains critical trends and patterns. What is variance? Variance measures the data spread around the mean, and features with low variance indicate little variation in their values. These features often need to be more formal for subsequent analysis and can hinder model performance. What is Principal Component Analysis (PCA)? PCA identifies and uses the principal components (directions that maximize variance and are orthogonal to each other) to effectively project data into a lower-dimensional space. This process begins with standardizing the original variables, ensuring their equal contribution to the analysis by normalizing them to have a zero mean and unit variance. Step-by-Step Explanation of Principal Component Analysis Standardization: Normalize the data so each variable contributes equally, addressing PCA's sensitivity to variable scales. Covariance Matrix Computation: Compute the covariance matrix to understand how the variables of the input dataset deviate from the mean and to see if they are related (i.e., correlated). Finding Eigenvectors and Eigenvalues: Find the new axes (eigenvectors) that maximize variance (measured by eigenvalues), making sure they are orthogonal to show that variance can go in different directions. Sorting and Ranking: Prioritize eigenvectors (and thus principal components) by their ability to capture data variance, using eigenvalues as the metric of importance. Feature Vector Formation: Select a subset of eigenvectors based on their ranking to form a feature vector. This subset of eigenvectors forms the principal components. Transformation: Map the original data into this principal component space, enabling analysis or further machine learning in a more tractable, less noisy space. Dimensionality reduction using PCA Applications PCA is widely used in exploratory data analysis and predictive modeling. It is also applied in areas like image compression, genomics for pattern recognition, and financial data for uncovering latent patterns and correlations. PCA can help visualize complex datasets by reducing data dimensionality. It can also make machine learning algorithms more efficient by reducing computational costs and avoiding overfitting with high-dimensional data. 3. Independent Component Analysis (ICA) Independent Component Analysis (ICA) is a computational method in signal processing that separates a multivariate signal into additive, statistically independent subcomponents. Statistical independence is critical because Gaussian variables maximize entropy given a fixed variance, making non-Gaussianity a key indicator of independence. Originating from the work of Hérault and Jutten in 1985, ICA excels in applications like the "cocktail party problem," where it isolates distinct audio streams amid noise without prior source information. Example of the cocktail party problem The cocktail party problem involves separating original sounds, such as music and voice, from mixed signals recorded by two microphones. Each microphone captures a different combination of these sounds due to its varying proximity to the sound sources. ICA is distinct from methods like PCA because it focuses on maximizing statistical independence between components rather than merely de-correlating them. Principles Behind Independent Component Analysis The essence of ICA is its focus on identifying and separating independent non-Gaussian signals embedded within a dataset. It uses the fact that these signals are statistically independent and non-Gaussian to divide the mixed signals into separate parts from different sources. This demixing process is pivotal, transforming seemingly inextricable data (impossible to separate) into interpretable components. Two main strategies for defining component independence in ICA are the minimization of mutual information and non-Gaussianity maximization. Various algorithms, such as infomax, FastICA, and kernel ICA, implement these strategies through measures like kurtosis and negentropy. Algorithmic Process To achieve its goals, ICA incorporates several preprocessing steps: Centering adjusts the data to have a zero mean, ensuring that analyses focus on variance rather than mean differences. Whitening transforms the data into uncorrelated variables, simplifying the subsequent separation process. After these steps, ICA applies iterative methods to separate independent components, and it often uses auxiliary methods like PCA or singular value decomposition (SVD) to lower the number of dimensions at the start. This sets the stage for efficient and robust component extraction. By breaking signals down into basic, understandable parts, ICA provides valuable information and makes advanced data analysis easier, which shows its importance in modern signal processing and beyond. Let’s see some of its applications. Applications of ICA The versatility of ICA is evident across various domains: In telecommunications, it enhances signal clarity amidst interference. Finance benefits from its ability to identify underlying factors in complex market data, assess risk, and detect anomalies. In biomedical signal analysis, it dissects EEG or fMRI data to isolate neurological activity from artifacts (such as eye blinks). 4. Sequential Non-negative Matrix Factorization (NMF) Nonnegative matrix Factorization (NMF) is a technique in multivariate analysis and linear algebra in which a matrix V is factorized into two lower-dimensional matrices, W (basis matrix) and H (coefficient matrix), with the constraint that all matrices involved have no negative elements. This factorization works especially well for fields where the data is naturally non-negative, like genetic expression data or audio spectrograms, because it makes it easy to understand the parts. The primary aim of NMF is to reduce dimensionality and uncover hidden/latent structures in the data. Principle of Sequential Non-negative Matrix Factorization The distinctive aspect of Sequential NMF is its iterative approach to decomposing matrix V into W and H, making it adept at handling time-series data or datasets where the temporal evolution of components is crucial. This is particularly relevant in dynamic datasets or applications where data evolves. Sequential NMF responds to changes by repeatedly updating W and H, capturing changing patterns or features important in online learning, streaming data, or time-series analysis. In text mining, for example, V denotes a term-document matrix over time, where W represents evolving topics and H indicates their significance across different documents or time points. This dynamic representation allows the monitoring of trends and changes in the dataset's underlying structure. Procedure of feature extraction using NMF Applications The adaptability of Sequential NMF has led to its application in a broad range of fields, including: Medical Research: In oncology, Sequential NMF plays a pivotal role in analyzing genetic data over time, aiding in the classification of cancer types, and identifying temporal patterns in biomarker expression. Audio Signal Processing: It is used to analyze sequences of audio signals and capture the temporal evolution of musical notes or speech. Astronomy and Computer Vision: Sequential NMF tracks and analyzes the temporal changes in celestial bodies or dynamic scenes. 5. Linear Discriminant Analysis (LDA) Linear Discriminant Analysis (LDA) is a supervised machine learning technique used primarily for pattern classification, dimensionality reduction, and feature extraction. It focuses on maximizing class separability. Unlike PCA, which optimizes for variance regardless of class labels, LDA aims to find a linear combination of features that separates different classes. It projects data onto a lower-dimensional space using class labels to accomplish this. Imagine, for example, a dataset of two distinct groups of points spread in space; LDA aims to find a projection where these groups are as distinct as possible, unlike PCA, which would look for the direction of highest variance regardless of class distinction. This method is highly efficient in scenarios where the division between categories of data is to be accentuated. PCA Vs. LDA: What's the Difference? Assumptions of LDA Linear Discriminant Analysis (LDA) operates under assumptions essential for effectively classifying observations into predefined groups based on predictor variables. These assumptions, elaborated below, play a critical role in the accuracy and reliability of LDA's predictions. Multivariate Normality: Each class must follow a multivariate normal distribution (multi-dimensional bell curve). You can asses this through visual plots or statistical tests before applying LDA. Homogeneity of Variances (Homoscedasticity): Ensuring uniform variance across groups helps maintain the reliability of LDA's projections. Techniques like Levene's test can assess this assumption. Absence of Multicollinearity: LDA requires predictors to be relatively independent. Techniques like variance inflation factors (VIFs) can diagnose multicollinearity issues. Working Methodology of Linear Discriminant Analysis LDA transforms the feature space into a lower-dimensional one that maximizes class separability by: Calculating mean vectors for each class. Computing within-class and between-class scatter matrices to understand the distribution and separation of classes. Solving for the eigenvalues and eigenvectors that maximize the between-class variance relative to the within-class variance. This defines the optimal projection space to distinguish the classes. Tools like Python's Scikit-learn library simplify applying LDA with functions specifically designed to carry out these steps efficiently. Applications LDA's ability to reduce dimensionality while preserving as much of the class discriminatory information as possible makes it a powerful feature extraction and classification tool applicable across various domains. Examples: In facial recognition, LDA enhances the distinction between individual faces to improve recognition accuracy. Medical diagnostics benefit from LDA's ability to classify patient data into distinct disease categories, aiding in early and accurate diagnosis. In marketing, LDA helps segment customers for targeted marketing campaigns based on demographic and behavioral data. 6. Generalized Discriminant Analysis (GDA) Generalized Discriminant Analysis (GDA) extends linear discriminant analysis (LDA) into a nonlinear domain. It uses kernel functions to project input data vectors into a higher-dimensional feature space to capture complex patterns that LDA, limited to linear boundaries, might miss. These functions project data into a higher-dimensional space where inseparable classes in the original space can be distinctly separated. Step-by-step Explanation of Generalized Discriminant Analysis The core objective of GDA is to find a low-dimensional projection that maximizes the between-class scatter while minimizing the within-class scatter in the high-dimensional feature space. Let’s examine the GDA algorithm step by step: 1. Kernel Function Selection: First, choose an appropriate kernel function (e.g., polynomial, radial basis function (RBF)) that transforms the input data into a higher-dimensional space. 2. Kernel Matrix Computation: Compute the kernel matrix K, representing the high-dimensional dot products between all pairs of data points. This matrix is central to transforming the data into a feature space without explicitly performing the computationally expensive mapping. 3. Scatter Matrix Calculation in Feature Space: In the feature space, compute the within-class scatter matrix SW and the between-class scatter matrix SB, using the kernel matrix K to account for the data's nonlinear transformation. 4. Eigenvalue Problem: Solving this problem in the feature space identifies the projection vectors that best separate the classes by maximizing the SB/SW ratio. This step is crucial for identifying the most informative projections for class separation. 5. Projection: Use the obtained eigenvectors to project the input data onto a lower-dimensional space that maximizes class separability to achieve GDA's goal of improved class recognition. Applications GDA has been applied in various domains, benefiting from its ability to handle nonlinear patterns: Image and Video Recognition: GDA is used for facial recognition, object detection, and activity recognition in videos, where the data often exhibit complex, nonlinear relationships. Biomedical Signal Processing: In analyzing EEG, ECG signals, and other biomedical data, GDA helps distinguish between different physiological states or diagnose diseases. Text Classification and Sentiment Analysis: GDA transforms text data into a higher-dimensional space, effectively separating documents or sentiments that are not linearly separable in the original feature space. 7. Missing Values Ratio (MVR): Threshold Setting Datasets often contain missing values, which can significantly impact the effectiveness of dimensionality reduction techniques. One approach to addressing this challenge is to utilize a missing values ratio (MVR) thresholding technique for feature selection. Process of Setting Threshold for Missing Values The MVR for a feature is calculated as the percentage of missing values for data points. The optimal threshold is dependent on several factors, including the dataset’s nature and the intended analysis: Determining the Threshold: Use statistical analyses, domain expertise, and exploratory data analysis (e.g., histograms of missing value ratios) to identify a suitable threshold. This decision balances retaining valuable data against excluding features that could introduce bias or noise. Implications of Threshold Settings: A high threshold may retain too many features with missing data, complicating the analysis. Conversely, a low threshold could lead to excessive data loss. Regularly, thresholds between 20% to 60% are considered, but this range varies widely based on the data context and analysis goals. Contextual Considerations: The dataset's specific characteristics and the chosen dimensionality reduction technique influence the threshold setting. Methods sensitive to data sparsity or noise may require a lower MVR threshold. Example: In a dataset with 100 observations, a feature with 75 missing values has an MVR of 75%. If the threshold is set at 70%, this feature would be considered for removal. Applications High-throughput Biological Data Analysis: Technical limitations often render Gene expression data incomplete. Setting a conservative MVR threshold may preserve crucial biological insights by retaining genes with marginally incomplete data. Customer Data Analysis: Customer surveys may have varying completion rates across questions. MVR thresholding identifies which survey items provide the most complete and reliable data, sharpening customer insights. Social Media Analysis: Social media data can be sparse, with certain users' entries missing. MVR thresholding can help select informative features for user profiling or sentiment analysis. 8. Low Variance Filter A low variance filter is a straightforward preprocessing technique aimed at reducing dimensionality by eliminating features with minimal variance, focusing analysis on more informative aspects of the dataset. Steps for Implementing a Low Variance Filter Calculate Variance: For each feature in the dataset, compute the variance. Prioritize scaling or normalizing data to ensure variance is measured on a comparable basis across all features. Set Threshold: Define a threshold for the minimum acceptable variance. This threshold often depends on the specific dataset and analysis objectives but typically ranges from a small percentage of the total variance observed across features. Feature Selection: Exclude features with variances below the threshold. Tools like Python's `pandas` library or R's `caret` package can efficiently automate this process. Applications of Low Variance Filter Across Domains Sensor Data Analysis: Sensor readings might exhibit minimal fluctuation over time, leading to features with low variance. Removing these features can help focus on the sensor data's more dynamic aspects. Image Processing: Images can contain features representing background noise. These features often have low variance and can be eliminated using the low variance filter before image analysis. Text Classification: Text data might contain stop words or punctuation marks that offer minimal information for classification. The low variance filter can help remove such features, improving classification accuracy. 9. High Correlation Filter The high correlation filter is a crucial technique for addressing feature redundancy. Eliminating highly correlated features optimizes datasets for improved model accuracy and efficiency. Steps for Implementing a High Correlation Filter Compute Correlation Matrix: Assess the relationship between all feature pairs using an appropriate correlation coefficient, such as Pearson for continuous features (linear relationships) and Spearman for ordinal (monotonic relationships). Define Threshold: Establish a correlation coefficient threshold above highly correlated features. A common threshold of 0.8 or 0.9 may vary based on specific model requirements and data sensitivity. Feature Selection: Identify sets of features whose correlation exceeds the threshold. From each set, retain only one feature based on criteria like predictive power, data completeness, or domain relevance and remove the others. Applications Financial Data Analysis: Stock prices or other financial metrics might exhibit a high correlation, often reflecting market trends. The high correlation filter can help select a representative subset of features for financial modeling. Bioinformatics: Gene expression data can involve genes with similar functions, leading to high correlation. Selecting a subset of uncorrelated genes can be beneficial for identifying distinct biological processes. Recommendation Systems: User profiles often contain correlated features like similar purchase history or browsing behavior. The high correlation filter can help select representative features to build more efficient recommendation models. While the Low Variance Filter method removes features with minimal variance, discarding data points that likely don't contribute much information, the High Correlation Filter approach identifies and eliminates highly correlated features. This process is crucial because two highly correlated features carry similar information, increasing redundancy within the model. 10. Forward Feature Construction Forward Feature Construction (FFC) is a methodical approach to feature selection, designed to incrementally build a model by adding features that offer the most significant improvement. This technique is particularly effective when the relationship between features and the target variable is complex and needs to be fully understood. Algorithm for Forward Feature Construction Initiate with a Null Model: Start with a baseline model without any predictors to establish a performance benchmark. Evaluation Potential Additions: For each candidate feature outside the model, assess potential performance improvements by adding that feature. Select the Best Feature: Incorporate the feature that significantly improves performance. Ensure the model remains interpretable and manageable. Iteration: Continue adding features until further additions fail to offer significant gains, considering computational efficiency and the risk of diminishing returns. Practical Considerations and Implementation Performance Metrics: To gauge improvements, use appropriate metrics, such as the Akaike Information Criterion (AIC) for regression or accuracy and the F1 score for classification, adapting the choice of metric to the model's context. Challenges: Be mindful of computational demands and the potential for multicollinearity. Implementing strategies to mitigate these risks, such as pre-screening features or setting a cap on the number of features, can be crucial. Tools: Leverage software tools and libraries (e.g., R's `stepAIC` or Python's `mlxtend.SequentialFeatureSelector`) that support efficient FFC application and streamline feature selection. Applications of FFC Across Domains Clinical Trials Prediction: In clinical research, FFC facilitates the identification of the most predictive biomarkers or clinical variables from a vast dataset, optimizing models for outcome prediction. Financial Modeling: In financial market analysis, this method distills a complex set of economic indicators down to a core subset that most accurately forecasts market movements or financial risk. 11. Backward Feature Elimination Backward Feature Elimination (BFE) systematically simplifies machine learning models by iteratively removing the least critical features, starting with a model that includes the entire set of features. This technique is particularly suited for refining linear and logistic regression models, where dimensionality reduction can significantly improve performance and interpretability. Algorithm for Backward Feature Elimination Initialize with Full Model: Construct a model incorporating all available features to establish a comprehensive baseline. Identify and Remove Least Impactful Feature: Determine the feature whose removal least affects or improves the model's predictive performance. Use metrics like p-values or importance scores to eliminate it from the model. Performance Evaluation: After each removal, assess the model to ensure performance remains robust. Utilize cross-validation or similar methods to validate performance objectively. Iterative Optimization: Continue this evaluation and elimination process until further removals degrade model performance, indicating that an optimal feature subset has been reached. Learn how to validate the performance of your ML model in this guide to validation model performance with Encord Active. Practical Considerations for Implementation Computational Efficiency: Given the potentially high computational load, especially with large feature sets, employ strategies like parallel processing or stepwise evaluation to simplify the Backward Feature Elimination (BFE) process. Complex Feature Interactions: Special attention is needed when features interact or are categorical. Consider their relationships to avoid inadvertently removing significant predictors. Applications Backward Feature Elimination is particularly useful in contexts like: Genomics: In genomics research, BFE helps distill large datasets into a manageable number of significant genes to improve understanding of genetic influences on diseases. High-dimensional Data Analysis: BFE simplifies complex models in various fields, from finance to the social sciences, by identifying and eliminating redundant features. This could reduce overfitting and improve the model's generalizability. While Forward Feature Construction is beneficial for gradually building a model by adding one feature at a time, Backward Feature Elimination is advantageous for models starting with a comprehensive set of features and needing to identify redundancies. 12. Autoencoders Autoencoders are a unique type of neural network used in deep learning, primarily for dimensionality reduction and feature learning. They are designed to encode inputs into a compressed, lower-dimensional form and reconstruct the output as closely as possible to the original input. This process emphasizes the encoder-decoder structure. The encoder reduces the dimensionality, and the decoder attempts to reconstruct the input from this reduced encoding. How Does Autoencoders Work? They achieve dimensionality reduction and feature learning by mimicking the input data through encoding and decoding. 1. Encoding: Imagine a bottle with a narrow neck in the middle. The data (e.g., an image) is the input that goes into the wide top part of the bottle. The encoder acts like this narrow neck, compressing the data into a smaller representation. This compressed version, often called the latent space representation, captures the essential features of the original data. The encoder is typically made up of multiple neural network layers that gradually reduce the dimensionality of the data. The autoencoder learns to discard irrelevant information and focus on the most important characteristics by forcing the data through this bottleneck. 2. Decoding: Now, imagine flipping the bottle upside down. The decoder acts like the wide bottom part, trying to recreate the original data from the compressed representation that came through the neck. The decoder also uses multiple neural network layers, but this time, it gradually increases the data's dimensionality, aiming to reconstruct the original input as accurately as possible. Variants and Advanced Applications Sparse Autoencoders: Introduce regularization terms to enforce sparsity in the latent representation, enhancing feature selection. Denoising Autoencoders: Specifically designed to remove noise from data, these autoencoders learn to recover clean data from noisy inputs, offering superior performance in image and signal processing tasks. Variational Autoencoders (VAEs): VAEs make new data samples possible by treating the latent space as a probabilistic distribution. This opens up new ways to use generative modeling. Training Nuances Autoencoders use optimizers like Adam or stochastic gradient descent (SGD) to improve reconstruction accuracy by improving their weights through backpropagation. Overfitting prevention is integral and can be addressed through methods like dropout, L1/L2 regularization, or a validation set for early stopping. Applications Autoencoders have a wide range of applications, including but not limited to: Dimensionality Reduction: Similar to PCA but more powerful (as non-linear alternatives), autoencoders can perform non-linear dimensionality reductions, making them particularly useful for preprocessing steps in machine learning pipelines. Image Denoising: By learning to map noisy inputs to clean outputs, denoising autoencoders can effectively remove noise from images, surpassing traditional denoising methods in efficiency and accuracy. Generative modeling: Variational autoencoders (VAEs) can make new data samples similar to the original input data by modeling the latent space as a continuous probability distribution. (e.g., Generative Adversarial Networks (GANs)). Impact of Dimensionality Reduction in Smart City Solutions Automotus is a company at the forefront of using AI to revolutionize smart city infrastructure, particularly traffic management. They achieve this by deploying intelligent traffic monitoring systems that capture vast amounts of video data from urban environments. However, efficiently processing and analyzing this high-dimensional data presents a significant challenge. This is where dimensionality reduction techniques come into play. The sheer volume of video data generated by Automotus' traffic monitoring systems necessitates dimensionality reduction techniques to make data processing and analysis manageable. PCA identifies the most significant features in the data (video frames in this case) and transforms them into a lower-dimensional space while retaining the maximum amount of variance. This allows Automotus to extract the essential information from the video data, such as traffic flow patterns, vehicle types, and potential congestion points, without analyzing every pixel. Partnering with Encord, Automotus led to a 20% increase in model accuracy and a 35% reduction in dataset size. This collaboration focused on dimensionality reduction, leveraging Encord Annotate’s flexible ontology, quality control capabilities, and automated labeling features. That approach helped Automotus reduce infrastructure constraints, improve model performance to provide better data to clients, and reduce labeling costs. Efficiency directly contributes to Automotus's business growth and operational scalability. The team used Encord Active to visually inspect, query, and sort their datasets to remove unwanted and poor-quality data with just a few clicks, leading to a 35% reduction in the size of the datasets for annotation. This enabled the team to cut their labeling costs by over a third. Interested in learning more? Read the full story on Encord's website for more details. Dimensionality Reduction Technique: Key Takeaways Dimensionality reduction techniques simplify models and enhance computational efficiency. They help manage the "curse of dimensionality," improving model generalizability and reducing overfitting risk. These techniques are used for feature selection and extraction, contributing to better model performance. They are applied in various fields, such as image and speech recognition, financial analysis, and bioinformatics, showcasing their versatility. By reducing the number of input variables, these methods ensure models are computationally efficient and capture essential data patterns for more accurate predictions.
March 21
10 min
Diffusion Transformer (DiT) Models: A Beginner’s Guide
What is a Diffusion Transformer (DiT)? Diffusion Transformer (DiT) is a class of diffusion models that are based on the transformer architecture. Developed by William Peebles at UC Berkeley and Saining Xie at New York University, DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer. Introduction to Diffusion Models Diffusion models are a type of generative model that simulates a Markov chain to transition from a simple prior distribution to the data distribution. The process is akin to a particle undergoing Brownian motion, where each step is a small random walk. This is why they are called “diffusion” models. Diffusion models have been used in various applications such as denoising, super-resolution, and inpainting. One of the key advantages of diffusion models is their ability to generate high-quality samples, which makes them particularly useful in tasks such as image synthesis. Convolutional U-NET Architecture The U-Net architecture is a type of convolutional neural network (CNN) that was developed for biomedical image segmentation. The architecture is designed like a U-shape, hence the name U-Net. It consists of a contracting path (encoder) to capture context and a symmetric expanding path (decoder) for precise localization. The U-Net architecture is unique because it concatenates feature maps from the downsampling path with feature maps from the upsampling path. This allows the network to use information from both the context and localization, enabling it to make more accurate predictions. Vision Transformers Vision Transformers (ViT) are a recent development in the field of computer vision that apply transformer models, originally designed for natural language processing tasks, to image classification tasks. Unlike traditional convolutional neural networks (CNNs) that process images in a hierarchical manner. ViTs treat images as a sequence of patches and capture global dependencies between them. This allows them to model long-range, pixel-level interactions. One of the key advantages of ViTs is their scalability. They can be trained on large datasets and can benefit from larger input image sizes. For more information, read the blog Introduction to Vision Transformers (ViT). Classifier-free Guidance Classifier-free guidance refers to the approach of guiding a model’s learning process without the use of explicit classifiers. This can be achieved through methods such as self-supervision, where the model learns to predict certain aspects of the data from other aspects, or through reinforcement learning, where the model learns to perform actions that maximize a reward signal. Classifier-free guidance can be particularly useful in situations where labeled data is scarce or expensive to obtain. It allows the model to learn useful representations from the data itself, without the need for explicit labels. Understanding Latent Diffusion Models (LDMs) Latent Diffusion Models (LDMs) are a type of generative model that learn to generate data by modeling it as a diffusion process. This process begins with a simple prior, such as Gaussian noise, and gradually transforms it into the target distribution through a series of small steps. Each step is guided by a neural network, which is trained to reverse the diffusion process. LDMs have been successful in generating high-quality samples in various domains, including images, text, and audio. For more information, read the official paper, High-Resolution Image Synthesis with Latent Diffusion Models. Convolutional U-NET Backbone: Disadvantages Convolutional U-NETs have been a staple in many computer vision tasks due to their ability to capture local features and maintain spatial resolution. However, they have certain limitations. For one, they often struggle with capturing long-range dependencies and global context in the input data. This is because the receptive field of a convolutional layer is local and finite, and increasing it requires deeper networks and larger filters, which come with their own set of challenges. Moreover, the convolution operation in U-NETs is translation invariant, which means it treats a feature the same regardless of its position in the image. This can be a disadvantage in tasks where the absolute position of features is important. Shifting towards Transformer Backbone Transformers, originally designed for natural language processing tasks, have shown great potential in computer vision tasks. Unlike convolutional networks, transformers can model long-range dependencies without the need for deep networks or large filters. This is because they use self-attention mechanisms, which allow each element in the input to interact with all other elements, regardless of their distance. Moreover, transformers are not translation invariant, which means they can capture the absolute position of features. This is achieved through the use of positional encodings, which add information about the position of each element in the input. Evolution of Latent Patches The concept of latent patches evolved from the need to make transformers computationally feasible for high-resolution images. Applying transformers directly to the raw pixels of high-resolution images is computationally expensive because the complexity of self-attention is quadratic in the number of elements. To overcome this, the image is divided into small patches, and transformers are applied to these patches. This significantly reduces the number of elements and hence the computational complexity. This allows transformers to capture both local features within each patch and global context across patches. Diffusion Transformers (DiT) Vs. Vision Transformers (ViT) While both DiT and ViT use transformers as their backbone and operate on latent patches, they differ in how they generate images and their specific architectural details. Diffusion Transformers (DiT) DiT uses transformers in a latent diffusion process, where a simple prior (like Gaussian noise) is gradually transformed into the target image. This is done by reversing the diffusion process guided by a transformer network. An important aspect of DiT is the concept of diffusion timesteps. These timesteps represent the stages of the diffusion process, and the transformer network is conditioned on the timestep at each stage. This allows the network to generate different features at different stages of the diffusion process. DiT can also be conditioned on ‘class labels’, allowing it to generate images of specific classes. Vision Transformers (ViT) ViT uses transformers to directly generate the image in an autoregressive manner, where each patch is generated one after the other, conditioned on the previously generated patches. A key component of ViT is the use of adaptive layer norm layers (adaLN). These layers adaptively scale and shift the features based on the statistics of the current batch, which helps in stabilizing the training and improving the model’s performance. While both approaches have their strengths and weaknesses, they represent two promising directions for leveraging transformers in generative modeling of images. The choice between DiT and ViT would depend on the specific requirements of the task at hand. For instance, if the task requires generating images of specific classes, DiT might be a better choice due to its ability to condition on class labels. On the other hand, if the task requires generating high-resolution images, ViT might be more suitable due to its use of adaLN layers, which can help in stabilizing the training of large models. Scalable Diffusion Models with Transformers Scalable Diffusion Models with Transformers (DiT) leverage the power of transformers to handle complex tasks involving large-scale data. The scalability of these models allows them to maintain or even improve their performance as the size of the input data increases. This makes them particularly suited for tasks such as natural language processing, image recognition, and other applications where the amount of input data can vary greatly. Here are some of the features of scalable diffusion models: Gflops - Forward Pass Measurement Gflops, short for gigaflops, is a unit of measurement that quantifies the performance of a computer’s floating-point operations. In the context of machine learning and neural networks, the forward pass measurement in Gflops is crucial as it provides an estimate of the computational resources required for a single forward pass through the network. This measurement is particularly important when dealing with large-scale networks or data, where computational efficiency can significantly impact the feasibility and speed of model training. Lower Gflops indicates a more efficient network in terms of computational resources, which can be a critical factor in resource-constrained environments or real-time applications. Network Complexity vs. Sample Quality The complexity of a neural network is often directly related to the quality of the samples it produces. More complex networks, which may have more layers or more neurons per layer, tend to produce higher quality samples. However, this increased complexity comes at a cost. More complex networks require more computational resources, both in terms of memory and processing power, and they often take longer to train. Conversely, simpler networks are more computationally efficient and faster to train, but they may not capture the nuances of the data as well, leading to lower quality samples. Striking the right balance between network complexity and sample quality is a key challenge in the design of effective neural networks. Variational Autoencoder (VAE)’s Latent Space In a Variational Autoencoder (VAE), the latent space is a lower-dimensional space into which the input data is encoded. This encoding process is a form of dimensionality reduction, where high-dimensional input data is compressed into a lower-dimensional representation. The latent space captures the essential characteristics of the data, and it is from this space that new samples are generated during the decoding process. The quality of the VAE’s output is largely dependent on how well the latent space captures the underlying structure of the input data. If the latent space is too small or not well-structured, the VAE may not be able to generate high-quality samples. If the latent space is well-structured and of appropriate size, the VAE can generate high-quality samples that accurately reflect the characteristics of the input data. Scalability of DiT Scalability is an important feature of Diffusion models with Transformers (DiT). As the size of the input data increases, the model should be able to maintain or improve its performance. This involves efficient use of computational resources and maintaining the quality of the generated samples. For example, in natural language processing tasks, the size of the input data (i.e., the number of words or sentences) can vary greatly. A scalable DiT model should be able to handle these variations in input size without a significant drop in performance. Furthermore, as the amount of available data continues to grow, the ability of DiT models to scale effectively will become increasingly important. For more information, read the official paper, Scalable Diffusion Models with Transformers. DiT Scaling Methods There are two primary methods for scaling DiT models: scaling the model size and scaling the number of tokens. Scaling Model Size Scaling the model size involves increasing the complexity of the model, typically by adding more layers or increasing the number of neurons in each layer. This can improve the model’s ability to capture complex patterns in the data, leading to improved performance. However, it also increases the computational resources required to train and run the model. Therefore, it’s important to find a balance between model size and computational efficiency. Scaling Tokens Scaling the number of tokens involves increasing the size of the input data that the model can handle. This is particularly relevant for tasks such as natural language processing, where the input data (i.e., text) can vary greatly in length. By scaling the number of tokens, a DiT model can handle longer texts without a significant drop in performance. However, similar to scaling the model size, scaling the number of tokens also increases the computational resources required, so a balance must be found. Diffusion Transformers Generalized Architecture Spatial Representations The model first inputs spatial representations through a network layer, converting spatial inputs into a sequence of tokens. This process allows the model to handle the spatial information present in the image data. It’s a crucial step as it transforms the input data into a format that the transformer can process effectively. Positional Embeddings Positional embeddings are a critical component of the transformer architecture. They provide the model with information about the position of each token in the sequence. In DiTs, standard Vision Transformer based positional embeddings are applied to all input tokens. This process helps the model understand the relative positions and relationships between different parts of the image. DiT Block Design In a typical diffusion model, a U-Net convolutional neural network (CNN) learns to estimate the noise to be removed from an image. DiTs replace this U-Net with a transformer. This replacement shows that U-Net’s inductive bias is not necessary for the performance of diffusion models. Diffusion Transformer Architecture Variants of DiT blocks handle conditional information with the following blocks: In-context Conditioning In-context conditioning in DiTs involves the use of adaptive layer normalization (adaLN) to inject conditional information into the model. Cross-attention Block The cross-attention in DiTs bridges the interaction between the diffusion network and the image encoder. It mixes two different embedding sequences, allowing the model to capture both local and global information. Conditioning via Adaptive Layer Norm An adaptive layer normalization (adaLN) is used to condition the diffusion network on text representations, enabling parameter-efficient adaptation. Conditioning via Cross-attention Cross-attention is used to bridge the interaction between the diffusion network and the image encoder. It allows attention layers to adapt their behavior at different stages of the denoising process. Conditioning via Extra Input Tokens While there is limited information available on conditioning via extra input tokens in DiTs, it is known that DiTs with higher Gflops—through increased transformer depth/width or increased number of input tokens—consistently have lower FID. Model Size DiT models range from 33M to 675M parameters and 0.4 to 119 Gflops. They are borrowed from the ViT literature which found that jointly scaling-up depth and width works well. Transformer Decoder The transformer decoder is an architectural upgrade that replaces U-Net with vision transformers (ViT), showing U-Net inductive bias is not necessary for the performance of diffusion models. Training and Inference During training, a diffusion model takes an image to which noise has been added, a descriptive embedding, and an embedding of the current time step. The system learns to use descriptive embedding to remove the noise in successive time steps. At inference, it generates an image by starting with pure noise and a descriptive embedding and removing noise iteratively according to that embedding. Evaluation Metrics The quality of DiT’s output is evaluated according to Fréchet Interception Distance (FID), which measures how the distribution of a generated version of an image compares to the distribution of the original (lower is better). FID improves depending on the processing budget. On 256-by-256-pixel ImageNet images, a small DiT with 6 gigaflops of compute achieves 68.4 FID, a large DiT with 80.7 gigaflops achieves 23.3 FID, and the largest DiT with 119 gigaflops achieves 9.62 FID. A latent diffusion model that used a U-Net (104 gigaflops) achieves 10.56 FID. DiT-XL/2 Models: Trained Versions The DiT-XL/2 models are a series of generative models released by Meta. These models are trained on the ImageNet dataset, a large visual database designed for use in visual object recognition research. The XL/2 in the name refers to the resolution at which the models are trained, with two versions available: one for 512x512 resolution images and another for 256x256 resolution images. 512x512 Resolution on ImageNet The DiT-XL/2 model trained on ImageNet at a resolution of 512x512 uses classifier-free guidance scales of 6.0. The training process for this model took 3M steps. This high-resolution model is designed to handle complex images with intricate details. 256x256 Resolution on ImageNet The DiT-XL/2 model trained on ImageNet at a resolution of 256x256 uses classifier-free guidance scales of 4.0. The training process for this model took 7M steps. This model is optimized for standard resolution images and is more efficient in terms of computational resources. FID Comparisons of the Two Resolutions The DiT-XL/2 model trained at 256x256 resolution outperforms all prior diffusion models, achieving a state-of-the-art FID-50K of 2.27. This is a significant improvement over the previous best FID-50K of 3.60 achieved by the LDM (256x256) model. In terms of compute efficiency, the DiT-XL/2 model is also superior, requiring only 119 Gflops compared to the LDM-4 model’s 103 Gflops and ADM-U’s 742 Gflops. Scalable Diffusion Models with Transformers. At 512x512 resolution, the DiT-XL/2 model again outperforms all prior diffusion models, improving the previous best FID of 3.85 achieved by ADM-U to 3.04. In terms of compute efficiency, the DiT-XL/2 model requires only 525 Gflops, significantly less than ADM-U’s 2813 Gflops. You can find the DiT-XL/2 models on GitHub and run them on HuggingFace or in a Colab Notebook. Applications of Diffusion Transformer One of the notable applications of DiT is in image generation. Other applications include text summarizations, chatbots, recommendation engines, language translation, knowledge bases, etc. Let’s look at some notable SOTA models which use diffusion transformer architectures: OpenAI’s SORA Video generation models as world simulators OpenAI’s SORA is an AI model that can create realistic and imaginative scenes from text instructions. SORA is a diffusion model, which generates a video by starting off with one that looks like static noise and gradually transforms it by removing the noise over many steps. It can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt. SORA is capable of generating entire videos all at once or extending generated videos to make them longer. For more information, read the blog: OpenAI Releases New Text-to-Video Model, Sora Stable Diffusion 3 Scaling Rectified Flow Transformers for High-Resolution Image Synthesis Stable Diffusion 3 (SD3) is an advanced text-to-image generation model developed by Stability AI. SD3 combines a diffusion transformer architecture and flow matching. It generates high-quality images from textual descriptions. SD3 outperforms state-of-the-art text-to-image generation systems such as DALL·E 3, Midjourney v6, and Ideogram v1 in typography and prompt adherence, based on human preference evaluations. For more information, read the blog: Stable Diffusion 3: Multimodal Diffusion Transformer Model Explained. PixArt-ɑ PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Syntheis PixArt-α is a Transformer-based Text-to-Image (T2I) diffusion model. Its image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. PixArt-α supports high-resolution image synthesis up to 1024px resolution with low training cost6. It excels in image quality, artistry, and semantic control. Diffusion Transformer: Key Takeaways Class of Diffusion Models: Diffusion Transformers (DiT) are a novel class of diffusion models that leverage the transformer architecture. Improved Performance: DiT aims to improve the performance of diffusion models by replacing the commonly used U-Net backbone with a transformer. Impressive Scalability: DiT models have demonstrated impressive scalability properties, with higher Gflops consistently having lower Frechet Inception Distance (FID). Versatile Applications: DiT has been applied in various fields, including text-to-video models like OpenAI’s SORA, text-to-image generation models like Stable Diffusion 3, and Transformer-based Text-to-Image (T2I) diffusion models like PixArt-α.
March 18
8 min
What is Robotic Process Automation (RPA)?
Robotic process automation (RPA) promotes data-driven automation and digital transformation in modern industries, or “Industrial Revolution 4.0.” Data-driven automation primarily uses insights from data to program software to improve productivity on various tasks. On the other hand, digital transformation approaches create or modify existing products and services, modify businesses, and improve efficiency, customer experience, and overall competitiveness. Modern industries, such as finance, healthcare, manufacturing, and retail, depend on RPA for many automation processes. It is assumed that RPA will overtake approximately 40% of accounting tasks by 2025, indicating a significant shift within the industry. This prediction indicates industries need to adapt RPA to streamline their workflows. Introduction to Robotic Process Automation RPA is an automation technology that uses software robots or robotic actors to automate repetitive manual tasks. It implements a rigid set of predefined rules and actions to streamline tasks that don’t require human effort. It even leverages technologies like artificial intelligence (AI), the Internet of Things (IoT), and even robotics to achieve automation with intelligence and efficiency. RPA, coupled with data-driven AI approaches in the current industries, aims to reduce human workload. A straightforward example of RPA in a banking institution is automating repetitive tasks such as data entry for customer transactions, updating customer records, and transaction validations. These processes are well structured and require clear steps and guidelines. Using RPA for such tasks is appropriate as it streamlines the process, reduces processing time, and minimizes errors. RPA Workflow Likewise, it can be seamlessly integrated with other technologies like blockchain, cloud computing, AR, VR, etc. This improves their capabilities and enables greater productivity, cost savings, and scalability. The traditional way of automating, which involved heavy coding, macro recording, playback, integrating APIs, etc., was slow, complex, and required intensive programming. RPA, on the other hand, offers a sharp contrast. It addresses those issues for automation to be accessible to the masses with its less-code functionality, shallow learning curve, and adaptability. How Does Robotic Process Automation (RPA) Work? Implementing RPA typically follows a structured, four-step process: Understanding the process requires reading the documentation, observing the process, conducting interviews with stakeholders, and conducting user testing. These will provide a list of requirements that adhere to the task and factors affecting the process. Defining workflow automation requires designing the process according to the specific requirements and the complexity of tasks. Depending on the available tools, this may require using low-code platforms with intuitive drag-and-drop interfaces or more advanced systems incorporating machine learning to process unstructured data like text from emails or documents. Integrating with existing systems or processes ensures that RPA bots have the necessary access to perform tasks by interacting with databases, applications, and other digital platforms. Effective integration enables data flow and task execution within the automated workflow. Workflow monitoring and optimization are essential, as they involve overseeing the execution of RPA bots, tracking performance metrics, and identifying any anomalies or issues that may arise during operation. Proactive monitoring enables timely intervention and optimization, ensuring smooth and reliable automation processes. With these steps, you can effectively implement RPA in your workflow. So far, we have seen how RPA benefits repetitive and mundane tasks with a given set of rules. But there are instances where automation can be more than just defining workflows. Sometimes, RPA must reason and make decisions based on the circumstances or data provided. In the next section, we will explore the different types of RPA that satisfy the previous statement. Types of Robotic Process Automation (RPA) Let us briefly explore how RPA has evolved from a more traditional rule-based automation system to a more intelligent and dynamic data-driven automation technology. Traditional RPA Traditional RPA is designed to automate structured, rule-based tasks that do not require human judgment or decision-making. This approach utilizes predefined steps and workflows to execute repetitive tasks such as data entry, extraction, form filling, and transaction processing. Traditional RPA is highly effective in streamlining operations that follow a consistent pattern, reducing manual effort and error rates in tasks like invoice processing and routine data management. Applications and Implications of Traditional RPA Automate Logical and Straightforward Tasks: Traditional RPA is ideal for businesses that automate straightforward, voluminous tasks to increase efficiency and accuracy. For example, automating the invoice data entry process can significantly speed up accounts payable operations. Cognitive RPA Cognitive RPA extends the capabilities of traditional automation by integrating artificial intelligence (AI) and machine learning (ML) technologies. This advanced form of RPA can process structured and unstructured data, enabling it to perform tasks requiring contextual understanding, learning from patterns, and making decisions. RPA Revolution in the Healthcare Industry During COVID-19 Cognitive RPA applications include natural language processing (NLP) and large language models (LLMs) for interpreting human language, sentiment analysis for gauging customer feedback, and image recognition for analyzing visual data. Applications and Implications: Managing Complex Processes: Cognitive RPA is adept at handling complex processes such as customer service inquiries and analyzing large volumes of diverse data for insights because it adapts to changes and makes informed decisions. Context-aware Automation: It is suited for more complex challenges like automated customer support, where it can analyze inquiries, understand context, and provide personalized responses. Attended Automation Attended automation involves human collaboration as it works on the cues given by an operator. It is essentially a virtual assistant aiming to boost an individual’s productivity on repetitive tasks. It is also considered a front-end automation tool. It is quite useful for tasks that require human input and judgment to execute a process. Applications and Implications Human + RPA: It is effective for scheduling appointments, customer service interactions, and data validation, where human expertise complements automated processes. Front-office Tasks: It is primarily preferred for tasks such as receptions, flight booking, check-in automation, etc. Unattended Automation Unattended automation provides an end-to-end automated solution with no human involvement. The bots are independent and automate the entire workflow. In this case, the RPA is provided with a sequential and clear step to execute. This type of automation is suitable for executing long processes and works on dedicated machines. An orchestrator allows you to manage tasks by scheduling the entire workflow. You can trigger, monitor, and track your bots with an orchestrator. Applications and Implications They are suitable for backend processes. They can handle complex tasks like data processing, orchestrating various virtual machines, high-volume transaction processing, data migration between systems, etc. Hybrid Automation Hybrid automation combines attended and unattended automation. In this type of RPA, communication happens between both processes. Additionally, it combines human involvement and backend operations. The “attended bots” receive instructions from the human worker and initiate the process. If the process requires triggering unattended bots, these attended bots can do so. Upon triggering, the unattended bots do what they are best at—providing an end-to-end automated service. Once the task is completed, the unattended bots send the data or output to the attended bot, which notifies the human worker for further human input. Unattended robots handle tasks like data processing, report generation, etc. that don't require human involvement. On the other hand, attended robots handle tasks that require human attention, like gathering data. Applications and Implications Handling Complex Tasks: Hybrid automation excels in airport security check-in, order/delivery routing, inventory management, candidate screening, and interview scheduling. Robotic Process Automation (RPA) and Artificial Intelligence (AI) In the previous section, we discussed how powerful Cognitive RPA is and how it can handle complex tasks using tools like neural networks and other ML approaches. RPA and AI are powerful individually, but combined, they can achieve and excel much more. This section will discuss how AI can improve RPA capabilities and functionality. Integrating RPA with Computer Vision Let’s discuss in detail how AI can enhance the automation capabilities of RPA via computer vision (CV). To begin with, we must understand the complexities associated with an image dataset. Image data contains a lot of details and variability. Variability is one of the biggest concerns as it can portray diverse visual and content characteristics, including differences in size, shape, lighting, etc. Useful: Struggling with detecting and fixing image quality issues for your applications? Use our open-source toolkit, Encord Active OS, to detect image quality issues in this technical tutorial. The same object captured from different distances can portray different information. However, the same variability in the image contains rich information that, if leveraged properly, can help us get better information about the data. Example: Suppose you want to analyze thousands of images containing only cars and trucks for autonomous vehicles. You apply a segmentation mask and label each object with a respective class. You can use AI approaches such as CV to apply segmentation masks and assign labels to achieve this. The segmentation process can also represent cars and trucks with different colors for visualization. Once the segmentation masks are applied to each image, you can use RPA to automate various tasks. For example: It can automate the task of segregating cars and trucks into folders. It can extract and log individual images into a database or a spreadsheet RPA can trigger actions that initiate other required workflows or notifications based on the extracted data. You can see how versatile and beneficial RPA and AI can become when they are combined. You can use AI to perform complex tasks like image segmentation and annotation. However, RPA can build an automated pipeline based on the segmented and annotated images. Useful Read: What are the most prominent use cases of computer vision in robotics? Learn how machine vision powers eight use cases in robotics and automation from this resource. Now, let’s find out the additional advantages that RPA offers. Benefits of Robotic Process Automation (RPA) In this section, we will briefly discuss some of RPA's advantages. This will give you insight and help you make informed decisions about implementing RPA in your workflow and businesses. Below are some of the advantages. Low-code Development You can configure RPA software as it offers a UI drag-and-drop feature to define the automation process. This allows users to correctly, logically, and sequentially place the suitable automation component. It also facilitates rapid prototyping, a shallow learning curve, quicker deployment, and even improves collaboration. Increased Efficiency and Productivity RPA reduces human intervention and friction, allowing organizations to automate tasks consistently. This offers an efficient and streamlined workflow, which increases productivity. For example, automating invoice processing, payroll management, data migration, report generation, etc. Cost Savings through Automation RPA reduces human input and workload costs. This means routine work can be done cheaply, and human input can be used in other important areas. By automating repetitive tasks, RPA can save companies 30 to 50% in processing costs. Compared to manual work and traditional methods, this leads to a positive ROI within one year. Improved Accuracy and Compliance As we configure RPA bots with specific predefined rules, we constrain the bots to do that certain task. RPA can improve accuracy for repetitive tasks with well-defined rules by eliminating human error from fatigue and distractions. RPA software is easy to learn and deploy, and it offers the additional advantages of scalability and efficiency, economic friendliness, and workload reduction. However, it also has challenges. The following section will delve into some of RPA's challenges. Challenges of RPA We have seen how RPA benefits our repetitive, tedious, and mundane tasks. However, there can be instances where RPA can fail if the task is not correctly defined. Issues can also arise when working with data, among others. Let us now see four common challenges that RPA usually faces. Complexity of Process Identification When automating workflow, it is essential to understand the process because automating the wrong tasks can be detrimental. Carefully analyzing workflows and selecting well-defined, repetitive processes with clear inputs and outputs is essential for success. Integration with Legacy Systems Many organizations utilize older systems not designed for seamless integration with modern automation tools. This can require technical expertise and adaptation to overcome compatibility issues. Security and Compliance Concerns Integrating RPA introduces new access points and data flows. Robust security measures, including data encryption and access controls, are vital to ensure compliance and safeguard sensitive information. Resistance to Change and Organizational Culture Embracing automation often requires organizational shifts and employee training. Addressing concerns about job displacement, upskilling human workers, and fostering a culture of innovation are key to smooth adoption. These challenges often act as a roadblock that may hinder many workflow processes. But if these challenges are carefully addressed, they can help us break barriers and offer new solutions. Despite the challenges represented in this section, many industries have never refrained from implementing RPA in their workflow. You will learn some of these in the next section. Use Cases This section will discuss three primary industries that use RPA to streamline operations. The industries mentioned here have one thing in common: supply and demand. Because of this factor, freeing up the human workload and automating repetitive and exhausting processes is essential. Healthcare Healthcare organizations are one of the most demanding places where many things can be automated. Because of the ongoing patient visits, especially in hospitals, attending to patients remains a vital obligation compared to other mundane and repetitive tasks. Some of the areas that can be automated using RPA are: Claims Processing: Automating tasks like eligibility verification, data entry, and claims submission can save time, increase accuracy, and improve reimbursement cycles. Patient Scheduling and Registration: Automating appointment scheduling via the RPA app can reduce administrative burden. Medical Report Generation: Extracting high-volume data from various sources, such as imaging technologies, and generating standardized reports will reduce doctors' and clinicians' workload for patient care. Fraud Detection and Red-Teaming: Analyzing claim data to identify and flag potential fraudulent activity improves healthcare system security and integrity. As patient data requires high security, RPA can also automate various infiltration tests on the healthcare system to check its reliability and security. Retail With the rise of e-commerce and consumer demands, modern retail has enlarged its territory. Here are three ways in which the retail sector is using RPA: Order Processing and Fulfillment: Receiving orders from customers and their delivery is one of the critical jobs of retail. These can be automated using RPA, and customers can be notified regarding each process phase, such as order processing, shipping, etc. This enhances order accuracy and expedites delivery. Customer Service: Chatbots powered by RPA can handle routine inquiries, freeing up human agents for complex issues and improving customer experience. Price Management and Promotions: Automating tasks like price comparisons, discounts based on customer involvement, and campaign execution can promote dynamic pricing strategies and targeted promotions. Supply Chain Management RPA technology has a more significant impact on the supply chain, essentially orchestrating the exchange between various networks. It includes managing and storing raw materials, manufacturing, moving, delivering, and storing finished products in a warehouse. This is how RPA implementation enhances the supply chain. Purchase Order Processing: RPA automates vendor communication, purchase order generation, and approval cycles, streamlining procurement processes. Improving Supply Chain Planning: RPA can automate data analysis for forecasting and recent trends in markets and products. This eventually promotes better demand planning and inventory management. Logistics and Transportation: Using RPA to automate shipment tracking and route optimization improves logistics efficiency and reduces delays. Case Study: Role of Computer Vision in Enhancing RPA Capabilities in Healthcare In healthcare, a large part is devoted to imaging technology and visual data. For instance, radiology depends on X-rays, CT scans, and other imaging technologies to diagnose and treat patients. Some challenges revolve around this type of data: Image Analysis: Analyzing such images is hard and time-consuming. On average, a radiologist takes about 8 to 10 minutes, sometimes more if the image needs clarification. Workload Management: Understanding these images takes a lot of time, so it can be exhausting for radiologists to continuously read them and manage other obligations such as attending to the patient and counseling. Additionally, mental exhaustion can cause them to lose focus and make errors in diagnosis and treatment. Report Generation: This is another phase where radiologists struggle to focus on generating the right and precise patient report through the scan. Overcoming RPA Challenges by Using Computer Vision Traditional RPA can automate the above challenges with a predefined script but can be inefficient. However, certain tasks like fetching and organizing images can save radiologists time, but they might not be beneficial for complex tasks. This is because the automation script will mostly contain general steps. The software can make errors in anomalies and unclear images and provide the wrong solutions. For instance, the software may need to analyze the image and correctly interpret the data. Similarly, the software may fail to find anomalies, increase the rate of false positives and negatives, or misclassify the image. If those two cases occur, considerable errors in report generation could lead to the wrong diagnosis and treatment. Computer vision (CV) can be coupled with RPA to address these issues. CV is one of those approaches where you extract rich data representations from visual data. Using CV, RPA can utilize these representations that allow the software to interpret the images and make the right decision. With this combination of AI and RPA, radiologists can quickly receive and review accurate image analysis. This reduces their workload, allowing them to attend to patients or take on complex cases. Additionally, this system can generate reports that the radiologist can review and approve. In a nutshell, systems like this can improve radiologists' accuracy, efficiency, and workload management. Relevant Read: Viz.ai is a San Francisco-based health tech company. Learn how they accelerated the time from diagnosis to treatment using a data-centric CV platform to develop high-quality datasets in this case study. But on the downside, these AI systems need to be trained on a large dataset, which generally takes a lot of time. What’s Next: Cognitive Automation with Machine Vision? Cognitive automation has shown great potential, as it can efficiently handle complex tasks. As such, it holds great significance in Machine Vision. A subfield also uses cameras and sensors to get the input data. Modern industrial practices rely on the vision system to manufacture products and services. Cognitive automation with machine vision can enhance industries to make data-driven decisions, optimize operations, predict challenges, and improve efficiency across various sectors, such as scaling up and down based on requirements, strategic planning, etc. For instance: Companies developing autonomous vehicles use cameras and sensors to capture environmental data. Cognitive automation processes this data for decision-making, such as updating ML models with anomalies or new insights and integrating them into training simulations. Additionally, it can analyze familiar data, aiding predictive analytics. In the future, cognitive automation may facilitate vehicle-to-vehicle communication, enhancing safety. In manufacturing, vision systems are pivotal for product analysis and robot navigation. When combined with cognitive automation, new opportunities arise. For instance, it can identify bottlenecks like raw material shortages and automate orders. Furthermore, it can monitor product quality, gather user feedback, and suggest design improvements for future development. These technologies can promote human-machine collaboration, creating new spaces for innovation and engineering. This can ultimately lead to offering new and better product designs and services and reducing waste. Robotic Process Automation: Key Takeaways Robotic Process Automation as automation software and solutions rapidly transforms our work across different fields and processes. With advancements in AI, RPA implementation can be significantly enhanced to boost industrial productivity in a much more innovative way. As automation technology continues to evolve with RPA, the impact of automation solutions will only grow. They will reshape workflows and open doors for even greater automation possibilities. This will eventually drive research and development in many areas, promoting the betterment of human lives. While challenges exist, its potential for increased efficiency, reduced human error, accuracy, and cost savings is undeniable. Organizations can resolve these challenges by proactively adopting responsible development practices. They can use RPA to navigate the future of work effectively and unlock its full potential for success.
March 15
8 min
Top 10 Open Source Computer Vision Repositories
In this article, you will learn about the top 10 open-source Computer Vision repositories on GitHub. We discuss repository formats, their content, key learnings, and proficiency levels the repo caters to. The goal is to guide researchers, practitioners, and enthusiasts interested in exploring the latest advancements in Computer Vision. You will gain insights into the most influential open-source CV repositories to stay up-to-date with cutting-edge technology and potentially incorporate these resources into your projects. Readers can expect a comprehensive overview of the top Computer Vision repositories, including detailed descriptions of their features and functionalities. The article will also highlight key trends and developments in the field, offering valuable insights for those looking to enhance their knowledge and skills in Computer Vision. Here’s a list of the repositories we’re going to discuss: Awesome Computer Vision Segment Anything Model (SAM) Visual Instruction Tuning (LLaVA) LearnOpenCV Papers With Code Microsoft ComputerVision recipes Awesome-Deep-Vision Awesome transformer with ComputerVision CVPR 2023 Papers with Code Face Recognition What is GitHub? GitHub provides developers with a shared environment in which they can contribute code, collaborate on projects, and monitor changes. It also serves as a repository for open-source projects, allowing easy access to code libraries and resources created by the global developer community. Factors to Evaluate a Github Repository’s Health Before we list the top repositories for Computer Vision (CV), it is essential to understand how to determine a GitHub repository's health. The list below highlights a few factors you should consider to assess a repository’s reliability and sustainability: Level of Activity: Assess the frequency of updates by checking the number of commits, issues resolved, and pull requests. Contribution: Check the number of developers contributing to the repository. A large number of contributors signifies diverse community support. Documentation: Determine documentation quality by checking the availability of detailed readme files, support documents, tutorials, and links to relevant external research papers. New Releases: Examine the frequency of new releases. A higher frequency indicates continuous development. Responsiveness: Review how often the repository authors respond to issues raised by users. High responsiveness implies that the authors actively monitor the repository to identify and fix problems. Stars Received: Stars on GitHub indicate a repository's popularity and credibility within the developer community. Active contributors often attract more stars, showcasing their value and impact. Top 10 GitHub Repositories for Computer Vision (CV) Open source repositories play a crucial role in CV by providing a platform for researchers and developers to collaborate, share, and improve upon existing algorithms and models. These repositories host codebases, datasets, and documentation, making them valuable resources for enthusiasts, developers, engineers, and researchers. Let us delve into the top 10 repositories available on GitHub for use in Computer Vision. Disclaimer: Some of the numbers below may have changed after we published this blog post. Check the repository links to get a sense of the most recent numbers. #1 Awesome Computer Vision The awesome-php project inspired the Awesome Computer Vision repository, which aims to provide a carefully curated list of significant content related to open-source Computer Vision tools. Awesome Computer Vision Repository Repository Format You can expect to find resources on image recognition, object detection, semantic segmentation, and feature extraction. It also includes materials related to specific Computer Vision applications like facial recognition, autonomous vehicles, and medical image analysis. Repository Contents The repository is organized into various sections, each focusing on a specific aspect of Computer Vision. Books and Courses: Classic Computer Vision textbooks and courses covering foundational principles on object recognition, computational photography, convex optimization, statistical learning, and visual recognition. Research Papers and Conferences: This section covers research from conferences published by CVPapers, SIGGRAPH Papers, NIPS papers, and survey papers from Visionbib. Tools: It includes annotation tools such as LabelME and specialized libraries for feature detection, semantic segmentation, contour detection, nearest-neighbor search, image captioning, and visual tracking. Datasets: PASCAL VOC dataset, Ground Truth Stixel dataset, MPI-Sintel Optical Flow dataset, HOLLYWOOD2 Dataset, UCF Sports Action Data Set, Image Deblurring, etc. Pre-trained Models: CV models used to build applications involving license plate detection, fire, face, and mask detectors, among others. Blogs: OpenCV, Learn OpenCV, Tombone's Computer Vision Blog, Computer Vision for Dummies, Andrej Karpathy’s blog, Computer Vision Basics with Python Keras, and OpenCV. Key Learnings Visual Computing: Use the repo to understand the core techniques and applications of visual computing across various industries. Convex Optimization: Grasp this critical mathematical framework to enhance your algorithmic efficiency and accuracy in CV tasks. Simultaneous Localization and Mapping (SLAM): Explore the integration of SLAM in robotics and AR/VR to map and interact with dynamic environments. Single-view Spatial Understanding: Learn about deriving 3D insights from 2D imagery to advance AR and spatial analysis applications. Efficient Data Searching: Leverage nearest neighbor search for enhanced image categorization and pattern recognition performance. Aerial Image Analysis: Apply segmentation techniques to aerial imagery for detailed environmental and urban assessment. Proficiency Level Aimed at individuals with an intermediate to advanced understanding of Computer Vision. Commits: 206 | Stars: 19.8k | Forks: 4.1k | Author: Jia-Bin Huang | Repository Link. #2 SegmentAnything Model (SAM) segment-anything is maintained by Meta AI. The Segment Anything Model (SAM) is designed to produce high-quality object masks from input prompts such as points or boxes. Trained on an extensive dataset of 11 million images and 1.1 billion masks, SAM exhibits strong zero-shot performance on various segmentation tasks. segment-anything repository Repository Format The ReadMe.md file clearly mentions guides for installing these and running the model from prompts. Running SAM from this repo requires Python 3.8 or higher, PyTorch 1.7 or higher, and TorchVision 0.8 or higher. Repository Content The segment-anything repository provides code, links, datasets, etc. for running inference with the SegmentAnything Model (SAM). Here’s a concise summary of the content in the segment-anything repository: This repository provides: Code for running inference with SAM. Links to download trained model checkpoints. Downloadable dataset of images and masks used to train the model. Example notebooks demonstrating SAM usage. Lightweight mask decoder is exportable to the ONNX format for specialized environments. Key Learnings Some of the key learnings one can gain from the segment-anything repository are: Understanding Object Segmentation: Learn about object segmentation techniques and how to generate high-quality masks for objects in images. Explore using input prompts (such as points or boxes) to guide mask generation. Practical Usage of SAM: Install and use Segment Anything Model (SAM) for zero-shot segmentation tasks. Explore provided example notebooks to apply SAM to real-world images. Advanced Techniques: For more experienced users, explore exporting SAM’s lightweight mask decoder to ONNX format for specialized environments. Learn how to fine-tune the Segment Anything Model (SAM) through our comprehensive guide. Proficiency Level The Segment Anything Model (SAM) is accessible to users with intermediate to advanced Python, PyTorch, and TorchVision proficiency. Here’s a concise breakdown for users of different proficiency levels: Beginner | Install and Run: If you’re new to SAM, follow installation instructions, download a model checkpoint, and use the provided code snippets to generate masks from input prompts or entire images. Intermediate | Explore Notebooks: Dive into example notebooks to understand advanced usage, experiment with prompts, and explore SAM’s capabilities. Advanced | ONNX Export: For advanced users, consider exporting SAM’s lightweight mask decoder to ONNX format for specialized environments supporting ONNX runtime. Commits: 46 | Stars: 42.4k | Forks: 5k | Author: Meta AI Research | Repository Link. #3 Visual Instruction Tuning (LLaVA) Repository The LLaVA (Large Language and Vision Assistant) repository, developed by Haotian Liu, focuses on Visual Instruction Tuning. It aims to enhance large language and vision models, reaching capabilities comparable to GPT-4V and beyond. LLaVA demonstrates impressive multimodal chat abilities, sometimes even exhibiting behaviors similar to multimodal GPT-4 on unseen images and instructions. The project has seen several releases with unique features and applications, including LLaVA-NeXT, LLaVA-Plus, and LLaVA-Interactive. Visual Instruction Tuning (LLaVA) Repository Format The content in the LLaVA repository is primarily Python-based. The repository contains code, models, and other resources related to Visual Instruction Tuning. The Python files (*.py) are used to implement, train, and evaluate the models. Additionally, there may be other formats, such as Markdown for documentation, JSON for configuration files, and text files for logs or instructions. Repository Content LLaVA is a project focusing on visual instruction tuning for large language and vision models with GPT-4 level capabilities. The repository contains the following: LLaVA-NeXT: The latest release, LLaVA-NeXT (LLaVA-1.6), has additional scaling to LLaVA-1.5 and outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications. LLaVA-Plus: This version of LLaVA can plug and learn to use skills. LLaVA-Interactive: This release allows for an all-in-one demo for Image Chat, Segmentation, and Generation. LLaVA-1.5: This version of LLaVA achieved state-of-the-art results on 11 benchmarks, with simple modifications to the original LLaVA. Reinforcement Learning from Human Feedback (RLHF): LLaVA has been improved with RLHF to improve fact grounding and reduce hallucination. Key Learnings The LLaVA repository offers valuable insights in the domain of Visual Instruction Tuning. Some key takeaways include: Enhancing Multimodal Models: LLaVA focuses on improving large language and vision models to achieve capabilities comparable to GPT-4V and beyond. Impressive Multimodal Chat Abilities: LLaVA demonstrates remarkable performance, even on unseen images and instructions, showcasing its potential for multimodal tasks. Release Variants: The project has seen several releases, including LLaVA-NeXT, LLaVA-Plus, and LLaVA-Interactive, each introducing unique features and applications. Proficiency Level Catered towards intermediate and advanced levels Computer Vision engineers building vision-language applications. Commits: 446 | Stars: 14k | Forks: 1.5k | Author : Haotian Liu | Repository Link. #4 LearnOpenCV Satya Mallick maintains a repository on GitHub called LearnOpenCV. It contains a collection of C++ and Python codes related to Computer Vision, Deep Learning, and Artificial Intelligence. These codes are examples for articles shared on the LearnOpenCV.com blog. LearnOpenCV Repository Resource Format The resource format of the repository includes code for the articles and blogs. Whether you prefer hands-on coding or reading in-depth explanations, this repository has diverse resources to cater to your learning style. Repository Contents This repo contains code for Computer Vision, deep learning, and AI articles shared in OpenCV’s blogs, LearnOpenCV.com. You can choose the format that best suits your learning style and interests. Here are some popular topics from the LearnOpenCV repository: Face Detection and Recognition: Learn how to detect and recognize faces in images and videos using OpenCV and deep learning techniques. Object Tracking: Explore methods for tracking objects across video frames, such as using the Mean-Shift algorithm or correlation-based tracking. Image Stitching: Discover how to combine multiple images to create panoramic views or mosaics. Camera Calibration: Understand camera calibration techniques to correct lens distortion and obtain accurate measurements from images with OpenCV. Deep Learning Models: Use pre-trained deep learning models for tasks like image classification, object detection, and semantic segmentation. Augmented Reality (AR): Learn to overlay virtual objects onto real-world scenes using techniques such as marker-based AR. These examples provide practical insights into Computer Vision and AI, making them valuable resources for anyone interested in these fields! Key Learnings Apply OpenCV techniques confidently across varied industry contexts. Undertake hands-on projects using OpenCV that solidify your skills and theoretical understanding, preparing you for real-world Computer Vision challenges. Proficiency Level This repo caters to a wide audience: Beginner: Gain your footing in Computer Vision and AI with introductory blogs and simple projects. Intermediate: Elevate your understanding with more complex algorithms and applications. Advanced: Challenge yourself with cutting-edge research implementations and in-depth blog posts. Commits: 2,333 | Stars: 20.1k | Forks: 11.5k | Author: Satya Mallick | Repository Link. #5 Papers with Code Researchers from Meta AI are responsible for maintaining Papers with Code as a community project. No data is shared with any Meta Platforms product. Papers with Code Repository Repository Format The repository provides a wide range of Computer Vision research papers in various formats, such as: ResNet: A powerful convolutional neural network architecture with 2052 papers with code. Vision Transformer: Leveraging self-attention mechanisms, this model has 1229 papers with code. VGG: The classic VGG architecture boasts 478 papers with code. DenseNet: Known for its dense connectivity, it has 385 papers with code. VGG-16: A variant of VGG, it appears in 352 papers with code. Repository Contents This repository contains Datasets, Research Papers with Codes, Tasks, and all the Computer Vision-related research material on almost every segment and aspect of CV like The contents are segregated in the form of classified lists as follows: State-of-the-Art Benchmarks: The repository provides access to a whopping 4,443 benchmarks related to Computer Vision. These benchmarks serve as performance standards for various tasks and models. Diverse Tasks: With 1,364 tasks, Papers With Code covers a wide spectrum of Computer Vision challenges. Whether you’re looking for image classification, object tracking, or depth estimation, you'll find it here. Rich Dataset Collection: Explore 2,842 datasets curated for Computer Vision research. These datasets fuel advancements in ML and allow researchers to evaluate their models effectively. Massive Paper Repository: The platform hosts an impressive collection of 42,212 papers with codes. These papers contribute to cutting-edge research in Computer Vision. Key Learnings Here are some key learnings from the Computer Vision on Papers With Code: Semantic Segmentation: This task involves segmenting an image into regions corresponding to different object classes. There are 287 benchmarks and 4,977 papers with codes related to semantic segmentation. Object Detection: Object detection aims to locate and classify objects within an image. The section covers 333 benchmarks and 3,561 papers with code related to this task. Image Classification: Image classification involves assigning a label to an entire image. It features 464 benchmarks and 3,642 papers with code. Representation Learning: This area focuses on learning useful representations from data. There are 15 benchmarks and 3,542 papers with code related to representation learning. Reinforcement Learning (RL): While not specific to Computer Vision, there is 1 benchmark and 3,826 papers with code related to RL. Image Generation: This task involves creating new images. It includes 221 benchmarks and 1,824 papers with code. These insights provide a glimpse into the diverse research landscape within Computer Vision. Researchers can explore the repository to stay updated on the latest advancements and contribute to the field. Proficiency Levels A solid understanding of Computer Vision concepts and familiarity with machine learning and deep learning techniques are essential to make the best use of the Computer Vision section on Papers With Code. Here are the recommended proficiency levels: Intermediate: Proficient in Python, understanding of neural networks, can read research papers, and explore datasets. Advanced: Strong programming skills, deep knowledge, ability to contribute to research, and ability to stay updated. Benchmarks: 4,443 | Tasks: 1,364 | Datasets: 2,842 | Papers with Code: 42,212 #6 Microsoft / ComputerVision-Recipes The Microsoft GitHub organization hosts various open-source projects and samples across various domains. Among the many repositories hosted by Microsoft, the Computer Vision Recipes repository is a valuable resource for developers and enthusiasts interested in using Computer Vision technologies. Microsoft's Repositories Repository Format One key strength of Microsoft’s Computer Vision Recipes repository is its focus on simplicity and usability. The recipes are well-documented and include detailed explanations, code snippets, and sample outputs. Languages: The recipes are a range of programming languages, primarily Python (with some Jupyter Notebook examples), C#, C++, TypeScript, and JavaScript so that developers can use the language of their choice. Operating Systems: Additionally, the recipes are compatible with various operating systems, including Windows, Linux, and macOS. Repository Content Guidelines: The repository includes guidelines and recommendations for implementing Computer Vision solutions effectively. Code Samples: You’ll find practical code snippets and examples covering a wide range of Computer Vision tasks. Documentation: Detailed explanations, tutorials, and documentation accompany the code samples. Supported Scenarios: - Image Tagging: Assigning relevant tags to images. - Face Recognition: Identifying and verifying faces in images. - OCR (Optical Character Recognition): Extracting text from images. - Video Analytics: Analyzing videos for objects, motion, and events. Highlights| Multi-Object Tracking: Added state-of-the-art support for multi-object tracking based on the FairMOT approach described in the 2020 paper “A Simple Baseline for Multi-Object Tracking." . Key Learnings The Computer Vision Recipes repository from Microsoft offers valuable insights and practical knowledge in computer vision. Here are some key learnings you can expect: Best Practices: The repository provides examples and guidelines for building computer vision systems using best practices. You’ll learn about efficient data preprocessing, model selection, and evaluation techniques. Task-Specific Implementations: This section covers a variety of computer vision tasks, such as image classification, object detection, and image similarity. By studying these implementations, you’ll better understand how to approach real-world vision problems. Deep Learning with PyTorch: The recipes leverage PyTorch, a popular deep learning library. You’ll learn how to create and train neural networks for vision tasks and explore architectures and techniques specific to computer vision. Proficiency Level The Computer Vision Recipes repository caters to a wide range of proficiency levels, from beginners to experienced practitioners. Whether you’re just starting in computer vision or looking to enhance your existing knowledge, this repository provides practical examples and insights that can benefit anyone interested in building robust computer vision systems. Commits: 906 | Stars: 9.3k | Forks: 1.2k | Author: Microsoft | Repository Link. #7 Awesome-Deep-Vision The Awesome Deep Vision repository, curated by Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, and Taeksoo Kim, is a comprehensive collection of deep learning resources designed specifically for Computer Vision. This repository offers a well-organized collection of research papers, frameworks, tutorials, and other useful materials relating to Computer Vision and deep learning. Awesome-Deep-Vision Repository Repository Format The Awesome Deep Vision repository organizes its resources in a curated list format. The list includes various categories related to Computer Vision and deep learning, such as research papers, courses, books, videos, software, frameworks, applications, tutorials, and blogs. The repository is a valuable resource for anyone interested in advancing their knowledge in this field. Repository Content Here’s a closer look at the content and their sub-sections of the Awesome Deep Vision repository: Papers: This section includes seminal research papers related to Computer Vision. Notable topics covered include: ImageNet Classification: Papers like Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton’s work on image classification using deep convolutional neural networks. Object Detection: Research on real-time object detection, including Faster R-CNN and PVANET. Low-Level Vision: Papers on edge detection, semantic segmentation, and visual attention. Other resources are Computer Vision course lists, books, video lectures, frameworks, applications, tutorials, and insightful blog posts. Key Learnings The Awesome Deep Vision repository offers several valuable learnings for those interested in Computer Vision and deep learning: Stay Updated: The repository provides a curated list of research papers, frameworks, and tutorials. By exploring these resources, you can stay informed about the latest advancements in Computer Vision. Explore Frameworks: Discover various deep learning frameworks and libraries. Understanding their features and capabilities can enhance your ability to work with Computer Vision models. Learn from Research Papers: Dive into research papers related to Computer Vision. These papers often introduce novel techniques, architectures, and approaches. Studying them can broaden your knowledge and inspire your work. Community Collaboration: The repository is a collaborative effort by multiple contributors. Engaging with the community and sharing insights can lead to valuable discussions and learning opportunities. While the repository doesn’t directly provide model implementations, it is a valuable reference point for anyone passionate about advancing their Computer Vision and deep learning skills. Proficiency Level The proficiency levels that this repository caters to are: Intermediate: Proficiency in Python programming and awareness of deep learning frameworks. Advanced: In-depth knowledge of CV principles, mastery of frameworks, and ability to contribute to the community. Commits : 207 | Stars : 10.8k | Forks : 2.8k | Author : Jiwon Kim | Repository Link. #8 Awesome Transformer with Computer Vision (CV) The Awesome Visual Transformer repository is a curated collection of articles and resources on transformer models in Computer Vision (CV), maintained by dk-liang. The repository is a valuable resource for anyone interested in the intersection of visual transformers and Computer Vision (CV). Awesome-visual-transformer Repository Repository Format This repository (Awesome Transformer with Computer Vision (CV)) is a collection of research papers about transformers with vision. It contains surveys, arXiv papers, papers with codes on CVPR, and papers on many other subjects related to Computer Vision. It does not contain any coding. Repository Content This is a valuable resource for anyone interested in transformer models within the context of Computer Vision (CV). Here’s a brief overview of its content: Papers: The repository collects research papers related to visual transformers. Notable papers include: “Transformers in Vision”: A technical blog discussing vision transformers. “Multimodal learning with transformers: A survey”: An IEEE TPAMI paper. ArXiv Papers: The repository includes various arXiv papers, such as: “Understanding Gaussian Attention Bias of Vision Transformers” “TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation” Transformer for Classification: - Visual Transformer Stand-Alone Self-Attention in Vision Models: Designed for image recognition, by Ramachandran et al. in 2019. - Transformers for Image Recognition at Scale: Dosovitskiy et al. explore transformers for large-scale image recognition in 2021. Other Topics: The repository covers task-aware active learning, robustness against adversarial attacks, and person re-identification using locally aware transformers. Key Learnings Here are some key learnings from the Awesome Visual Transformer repository: Understanding Visual Transformers: The repository provides a comprehensive overview of visual transformers, including their architecture, attention mechanisms, and applications in Computer Vision. You’ll learn how transformers differ from traditional convolutional neural networks (CNNs) and their advantages. Research Papers and Surveys: Explore curated research papers and surveys on visual transformers. These cover topics like self-attention, positional encodings, and transformer-based models for image classification, object detection, and segmentation. Practical Implementations: The repository includes practical implementations of visual transformers. Studying these code examples will give you insights into how to build and fine-tune transformer-based models for specific vision tasks. Proficiency Level Aimed at Computer Vision researchers and engineers with a practical understanding of the foundational concepts of transformers. Commits: 259 | Stars: 3.2k | Forks: 390 | Author: Dingkang Liang | Repository Link. #9 Papers-with-Code: CVPR 2023 Repository The CVPR2024-Papers-with-Code repository, maintained by Amusi, is a comprehensive collection of research papers and associated open-source projects related to Computer Vision. It covers many topics, including machine learning, deep learning, image processing, and specific areas like object detection, image segmentation, and visual tracking. CVPR2024 Papers with Code Repository Repository Format The repository is an extensive collection of research papers and relevant codes organized according to different topics, including machine learning, deep learning, image processing, and specific areas like object detection, image segmentation, and visual tracking. Repository Content CVPR 2023 Papers: The repository contains a collection of papers presented at the CVPR 2023 conference. This year (2023), the conference received a record 9,155 submissions, a 12% increase over CVPR 2022, and accepted 2,360 papers for a 25.78% acceptance rate. Open-Source Projects: Along with the papers, the repository also includes links to the corresponding open-source projects. Organized by Topics: The papers and projects in the repository are organized by various topics such as Backbone, CLIP, MAE, GAN, OCR, Diffusion Models, Vision Transformer, Vision-Language, Self-supervised Learning, Data Augmentation, Object Detection, Visual Tracking, and numerous other related topics. Past Conferences: The repository also contains links to papers and projects from past CVPR conferences. Key Learnings Here are some key takeaways from the repository: Cutting-Edge Research: The repository provides access to the latest research papers presented at CVPR 2024. Researchers can explore novel techniques, algorithms, and approaches in Computer Vision. Practical Implementations: The associated open-source code allows practitioners to experiment with and implement state-of-the-art methods alongside research papers. This practical aspect bridges the gap between theory and application. Diverse Topics: The repository covers many topics, including machine learning, deep learning, image processing, and specific areas like object detection, image segmentation, and visual tracking. This diversity enables users to delve into various aspects of Computer Vision. In short, the repository is a valuable resource for staying informed about advancements in Computer Vision and gaining theoretical knowledge and practical skills. Proficiency Level While beginners may find the content challenging, readers with a solid foundation in Computer Vision can benefit significantly from this repository's theoretical insights and practical implementations. Commits: 642 | Stars: 15.2k | Forks: 2.4k | Author: Amusi | Repository Link. #10 Face Recognition This repository on GitHub provides a simple and powerful facial recognition API for Python. It lets you recognize and manipulate faces from Python code or the command line. Built using dlib’s state-of-the-art face recognition, this library achieves an impressive 99.38% accuracy on the Labeled Faces in the Wild benchmark. Face Recognition Repository Repository Format The content of the face_recognition repository on GitHub is primarily in Python. It provides a simple and powerful facial recognition API that allows you to recognize and manipulate faces from Python code or the command line. You can use this library to find faces in pictures, identify facial features, and even perform real-time face recognition with other Python libraries. Repository Content Here’s a concise list of the content within the face_recognition repository: Python Code Files: The repository contains Python code files that implement various facial recognition functionalities. These files include functions for finding faces in pictures, manipulating facial features, and performing face identification. Example Snippets: The repository provides example code snippets demonstrating how to use the library. These snippets cover tasks such as locating faces in images and comparing face encodings. Dependencies: The library relies on the dlib library for its deep learning-based face recognition. To use this library, you need to have Python 3.3+ (or Python 2.7), macOS or Linux, and dlib with Python bindings installed. Key Learnings Some of the key learnings from the face_recognition repository are: Facial Recognition in Python: It provides functions for locating faces in images, manipulating facial features, and identifying individuals. Deep Learning with dlib: You can benefit from the state-of-the-art face recognition model within dlib. Real-World Applications: By exploring the code and examples, you can understand how facial recognition can be applied in real-world scenarios. Applications include security, user authentication, and personalized experiences. Practical Usage: The repository offers practical code snippets that you can integrate into your projects. It’s a valuable resource for anyone interested in using facial data in Python. Proficiency Level Caters to users with a moderate-to-advanced proficiency level in Python. It provides practical tools and examples for facial recognition, making it suitable for those who are comfortable with Python programming and want to explore face-related tasks. Commits: 238 | Stars: 51.3k | Forks: 13.2k | Author: Adam Geitgey | Repository Link. Key Takeaways Open-source Computer Vision tools and resources greatly benefit researchers and developers in the CV field. The contributions from these repositories advance Computer Vision knowledge and capabilities. Here are the highlights of this article: Benefits of Code, Research Papers, and Applications: Code, research papers, and applications are important sources of knowledge and understanding. Code provides instructions for computers and devices, research papers offer insights and analysis, and applications are practical tools that users interact with. Wide Range of Topics: Computer Vision encompasses various tasks related to understanding and interpreting visual information, including image classification, object detection, facial recognition, and semantic segmentation. It finds applications in image search, self-driving cars, medical diagnosis, and other fields.
March 15
8 min
15 Interesting Github Repositories for Image Segmentation
A survey of Image segmentation GitHub Repositories shows how the field is rapidly advancing as computing power increases and diverse benchmark datasets emerge to evaluate model performance across various industrial domains. Additionally, with the advent of Transformer-based architecture and few-shot learning methods, the artificial intelligence (AI) community uses Vision Transformers (ViT) to enhance segmentation accuracy. The techniques involve state-of-the-art (SOTA) algorithms that only need a few labeled data samples for model training. With around 100 million developers contributing to GitHub globally, the platform is popular for exploring some of the most modern segmentation models currently available. This article explores the exciting world of segmentation by delving into the top 15 GitHub repositories, which showcase different approaches to segmenting complex images. But first, let’s understand a few things about image segmentation. What is Image Segmentation? Image segmentation is a computer vision (CV) task that involves classifying each pixel in an image. The technique works by clustering similar pixels and assigning them a relevant label. The method can be categorized into: Semantic segmentation—categorizes unique objects based on pixel similarity. Instance segmentation— distinguishes different instances of the same object category. For example, instance segmentation will recognize multiple individuals in an image as separate entities, labeling each person as “person 1”, “person 2”, “person 3”, etc. Semantic Segmentation (Left) and Instance Segmentation (Right) The primary applications of image segmentation include autonomous driving and medical imaging. In autonomous driving, segmentation allows the model to classify objects on the road. In medical imaging, segmentation enables healthcare professionals to detect anomalies in X-rays, MRIs, and CT scans. Want to know about best practices for image segmentation? Read our Guide to Image Segmentation in Computer Vision: Best Practices. Factors to Validate Github Repository’s Health Before we list the top repositories for image segmentation, it is essential to understand how to determine a GitHub repository's health. The list below highlights a few factors you should consider to assess a repository’s reliability and sustainability: Level of Activity: Assess the frequency of updates by checking the number of commits, issues resolved, and pull requests. Contribution: Check the number of developers contributing to the repository. A large number of contributors signifies diverse community support. Documentation: Determine documentation quality by checking the availability of detailed readme files, support documents, tutorials, and links to relevant external research papers. New Releases: Examine the frequency of new releases. A higher frequency indicates continuous development. Responsiveness: Review how often the repository authors respond to issues raised by users. High responsiveness implies that the authors actively monitor the repository to identify and fix problems. Stars Received: Stars on GitHub indicate a repository's popularity and credibility within the developer community. Active contributors often attract more stars, showcasing their value and impact. Top GitHub Repositories for Image Segmentation Due to image segmentation’s ability to perform advanced detection tasks, the AI community offers multiple open-source GitHub repositories comprising the latest algorithms, research papers, and implementation details. The following sections will overview the fifteen most interesting public repositories, describing their resource format and content, topics covered, key learnings, and difficulty level. #1. Awesome Referring Image Segmentation Referring image segmentation involves segmenting objects based on a natural language query. For example, the user can provide a phrase such as “a brown bag” to segment the relevant object within an image containing multiple objects. Referring image segmentation Resource Format The repository is a collection of benchmark datasets, research papers, and their respective code implementations. Repository Contents The repo comprises ten datasets, including ReferIt, Google-Ref, UNC, and UNC+, and 72 SOTA models for different referring image segmentation tasks. Topics Covered Traditional Referring Image Segmentation: In the repo, you will find frameworks or traditional referring image segmentation, such as LISA, for segmentation through large language models (LLMs). Interactive Referring Image Segmentation: Includes the interactive PhraseClick referring image segmentation model. Referring Video Object Segmentation: Consists of 18 models to segment objects within videos. Referring 3D Instance Segmentation: There are two models for referring 3D instance segmentation tasks for segmenting point-cloud data. Key Learnings Different Types of Referring Image Segmentation: Exploring this repo will allow you to understand how referring interactive, 3D instance, and video segmentation differ from traditional referring image segmentation tasks. Code Implementations: The code demonstrations will help you apply different frameworks to real-world scenarios. Proficiency Level The repo is for expert-level users with a robust understanding of image segmentation concepts. Commits: 71 | Stars: 501 | Forks: 54 | Author: Haoran MO | Repository Link. #2. Transformer-based Visual Segmentation Transformer-based visual segmentation uses the transformer architecture with the self-attention mechanism to segment objects. Transformer-based Visual Segmentation Resource Format The repo contains research papers and code implementations. Resource Contents It has several segmentation frameworks based on convolutional neural networks (CNNs), multi-head and cross-attention architectures, and query-based models. Topics Covered Detection Transformer (DETR): The repository includes models built on the DETR architecture that Meta introduced. Attention Mechanism: Multiple models use the attention mechanism for segmenting objects. Pre-trained Foundation Model Tuning: Covers techniques for tuning pre-trained models. Key Learnings Applications of Transformers in Segmentation: The repo will allow you to explore the latest research on using transformers to segment images in multiple ways. Self-supervised Learning: You will learn how to apply self-supervised learning methods to transformer-based visual segmentation. Proficiency Level This is an expert-level repository requiring an understanding of the transformer architecture. Commits: 13 | Stars: 549 | Forks: 40 | Author: Xiangtai Li | Repository Link. #3. Segment Anything The Segment Anything Model (SAM) is a robust segmentation framework by Meta AI that generates object masks through user prompts. Segment Anything Model Resource Format The repo contains the research paper and an implementation guide. Resource Contents It consists of Jupyter notebooks and scripts with sample code for implementing SAM and has three model checkpoints, each with a different backbone size. It also provides Meta’s own SA-1B dataset for training object segmentation models. Topics Covered How SAM Works: The paper explains how Meta developed the SAM framework. Getting Started Tutorial: The Getting Started guide helps you generate object masks using SAM. Key Learnings How to Use SAM: The repo teaches you how to create segmentation masks with different model checkpoints. Proficiency Level This is a beginner-level repo that teaches you about SAM from scratch. Commits: 46 | Stars: 42.8k | Forks: 5k | Author: Hanzi Mao | Repository Link. #4. Awesome Segment Anything The Awesome Segment Anything repository is a comprehensive survey of models using SAM as the foundation to segment anything. SAM mapping image features and prompt embeddings set for a segmentation mask Resource Format The repo is a list of papers and code. Resource Content It consists of SAM’s applications, historical development, and research trends. Topics Covered SAM-based Models: The repo explores the research on SAM-based frameworks. Open-source Projects: It also covers open-source models on platforms like HuggingFace and Colab. Key Learnings SAM Applications: Studying the repo will help you learn about use cases where SAM is relevant. Contemporary Segmentation Methods: It introduces the latest segmentation methods based on SAM. Proficiency Level This is an expert-level repo containing advanced research papers on SAM. Commits: 273 | Stars: 513 | Forks: 39 | Author: Chunhui Zhang | Repository Link. #5. Image Segmentation Keras The repository is a Keras implementation of multiple deep learning image segmentation models. SAM mapping image features and prompt embeddings set for a segmentation mask Resource Format Code implementations of segmentation models. Resource Content The repo consists of implementations for Segnet, FCN, U-Net, Resnet, PSPNet, and VGG-based segmentation models. Topics Covered Colab Examples: The repo demonstrates implementations through a Python interface. Installation: There is an installation guide to run the relevant modules. Key Learnings How to Use Keras: The repo will help you learn how to implement segmentation models in Keras. Fine-tuning and Knowledge Distillation: The repo contains sections that explain how to fine-tune pre-trained models and use knowledge distillation to develop simpler models. Proficiency Level The repo is an intermediate-level resource for those familiar with Python. Commits: 256 | Stars: 2.8k | Forks: 1.2k | Author: Divam Gupta | Repository Link. #6. Image Segmentation The repository is a PyTorch implementation of multiple segmentation models. R2U-Net Resource Format It consists of code and research papers. Resource Content The models covered include U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net. Topics Covered Architectures: The repo explains the models’ architectures and how they work. Evaluation Strategies: It tests the performance of all models using various evaluation metrics. Key Learnings PyTorch: The repo will help you learn about the PyTorch library. U-Net: It will familiarize you with the U-Net model, a popular framework for medical image segmentation. Proficiency Level This is an intermediate-level repo for those familiar with deep neural networks and evaluation methods in machine learning. Commits: 13 | Stars: 2.4k | Forks: 584 | Author: LeeJunHyun | Repository Link. #7. Portrait Segmentation The repository contains implementations of portrait segmentation models for mobile devices. Portrait Segmentation Resource Format The repo contains code and a detailed tutorial. Resource Content It consists of checkpoints, datasets, dependencies, and demo files. Topics Covered Model Architecture: The repo explains the architecture for Mobile-Unet, Deeplab V3+, Prisma-net, Portrait-net, Slim-net, and SINet. Evaluation: It reports the performance results of all the models. Key Learnings Portrait Segmentation Techniques: The repo will teach you about portrait segmentation frameworks. Model Development Workflow: It gives tips and tricks for training and validating models. Proficiency Level This is an expert-level repo. It requires knowledge of Tensorflow, Keras, and OpenCV. Commits: 405 | Stars: 624 | Forks: 135 | Author: Anilsathyan | Repository Link. #8. BCDU-Net The repository implements the Bi-Directional Convolutional LSTM with U-net (BCDU-Net) for medical segmentation tasks, including lung, skin lesions, and retinal blood vessel segmentation. BCDU-Net Architecture Resource Format The repo contains code and an overview of the model. Resource Content It contains links to the research paper, updates, and a list of medical datasets for training. It also provides pre-trained weights for lung, skin lesion, and blood vessel segmentation models. Topics Covered BCDU-Net Architecture: The repo explains the model architecture in detail. Performance Results: It reports the model's performance statistics against other SOTA frameworks. Key Learnings Medical Image Analysis: Exploring the repo will familiarize you with medical image formats and how to detect anomalies using deep learning models. BCDU-Net Development Principles: It explains how the BCDU-net model works based on the U-net architecture. You will also learn about the Bi-directional LSTM component fused with convolutional layers. Proficiency Level This is an intermediate-level repo. It requires knowledge of LSTMs and CNNs. Commits: 166 | Stars: 656 | Forks: 259 | Author: Reza Azad | Repository Link. #9.MedSegDiff The repository demonstrates the use of diffusion techniques for medical image segmentation. Diffusion Technique Resource Format It contains code implementations and a research paper. Resource Contents It overviews the model architecture and contains the brain tumor segmentation dataset. Topics Covered Model Structure: The repo explains the application of the diffusion method to segmentation problems. Examples: It contains examples for training the model on tumor and melanoma datasets. Key Learnings The Diffusion Mechanism: You will learn how the diffusion technique works. Hyperparameter Tuning: The repo demonstrates a few hyper-parameters to fine-tune the model. Proficiency Level This is an intermediate-level repo requiring knowledge of diffusion methods. Commits: 116 | Stars: 868 | Forks: 130 | Author: Junde Wu | Repository Link. #10. U-Net The repository is a Keras-based implementation of the U-Net architecture. U-Net Architecture Resource Format It contains the original training dataset, code, and a brief tutorial. Resource Contents The repo provides the link to the U-Net paper and contains a section that lists the dependencies and results. Topics Covered U-Net Architecture: The research paper in the repo explains how the U-Net model works. Keras: The topic page has a section that gives an overview of the Keras library. Key Learnings Data Augmentation: The primary feature of the U-net model is its use of data augmentation techniques. The repo will help you learn how the framework augments medical data for enhanced training. Proficiency Level This is a beginner-level repo requiring basic knowledge of Python. Commits: 17 | Stars: 4.4k | Forks: 2k | Author: Zhixuhao | Repository Link. #11. SOTA-MedSeg The repository is a detailed record of medical image segmentation challenges and winning models. Medical Imaging Segmentation Methods Resource Format The repo comprises research papers, code, and segmentation challenges based on different anatomical structures. Resource Contents It mentions the winning models for each year from 2018 to 2023 and provides their performance results on multiple segmentation tasks. Topics Covered Medical Image Segmentation: The repo explores models for segmenting brain, head, kidney, and neck tumors. Past Challenges: It lists older medical segmentation challenges. Key Learnings Latest Trends in Medical Image Processing: The repo will help you learn about the latest AI models for segmenting anomalies in multiple anatomical regions. Proficiency Level This is an expert-level repo requiring in-depth medical knowledge. Commits: 70 | Stars: 1.3k | Forks: 185 | Author: JunMa | Repository Link. #12. UniverSeg The repository introduces the Universal Medical Image Segmentation (UniverSeg) model that requires no fine-tuning for novel segmentation tasks (e.g. new biomedical domain, new image type, new region of interest, etc). UnverSeg Method Resource Format It contains the research paper and code for implementing the model. Resource Contents The research paper provides details of the model architecture and Python code with an example dataset. Topics Covered UniverSeg Development: The repo illustrates the inner workings of the UniverSeg model. Implementation Guidelines: A ‘Getting Started’ section will guide you through the implementation process. Key Learnings Few-shot Learning: The model employs few-shot learning methods for quick adaptation to new tasks. Proficiency Level This is a beginner-level repo requiring basic knowledge of few-shot learning. Commits: 31 | Stars: 441 | Forks: 41 | Author: Jose Javier | Repository Link. #13. Medical SAM Adapter The repository introduces the Medical SAM Adapter (Med-SA), which fine-tunes the SAM architecture for medical-specific domains. Med-SA Architecture Resource Format The repo contains a research paper, example datasets, and code for implementing Med-SA. Resource Contents The paper explains the architecture in detail, and the datasets relate to melanoma, abdominal, and optic-disc segmentation. Topics Covered Model Architecture: The research paper in the repo covers a detailed explanation of how the model works. News: It shares a list of updates related to the model. Key Learnings Vision Transformers (ViT): The model uses the ViT framework for image adaptation. Interactive Segmentation: You will learn how the model incorporates click prompts for model training. Proficiency Level The repo is an expert-level resource requiring an understanding of transformers. Commits: 95 | Stars: 759 | Forks: 58 | Author: Junde Wu (via Kids with Tokens) | Repository Link. #14. TotalSegmentator The repository introduces TotalSegmentator, a domain-specific medical segmentation model for segmenting CT images. Subtasks with Classes Resource Format The repo provides a short installation guide, code files, and links to the research paper. Resource Contents The topic page lists suitable use cases, advanced settings, training validation details, a Python API, and a table with all the class names. Topics Covered Total Segmentation Development: The paper discusses how the model works. Usage: It explains the sub-tasks the model can perform. Key Learnings Implementation Using Custom Datasets: The repo teaches you how to apply the model to unique medical datasets. nnU-Net: The model uses nnU-Net, a semantic segmentation model that automatically adjusts parameters based on input data. Proficiency Level The repo is an intermediate-level resource requiring an understanding of the U-Net architecture. Commits: 560 | Stars: 1.1k | Forks: 171 | Author: Jakob Wasserthal | Repository Link. #15. Medical Zoo Pytorch The repository implements a Pytorch-based library for 3D multi-modal medical image segmentation. Implementing Image Segmentation in PyTorch Resource Format It contains the implementation code and research papers for the models featured in the library. Resource Contents The repo lists the implemented architectures and has a Quick Start guide with a demo in Colab. Topics Covered 3D Segmentation Models: The library contains multiple models, including U-Net3D, V-net, U-Net, and MED3D. Image Data-loaders: It consists of data-loaders for fetching standard medical datasets. Key Learnings Brain Segmentation Performance: The research paper compares the performance of implemented architectures on brain sub-region segmentation. This will help you identify the best model for brain segmentation. COVID-19 Segmentation: The library has a custom model for detecting COVID-19 cases. The implementation will help you classify COVID-19 patients through radiography chest images. Proficiency Level This is an expert-level repo requiring knowledge of several 3D segmentation models. Commits: 122 | Stars: 1.6k | Forks: 288 | Author: Adaloglou Nikolas | Repository Link. GitHub Repositories for Image Segmentation: Key Takeaways While object detection and image classification models dominate the CV space, the recent rise in segmentation frameworks signals a new era for AI in various applications. Below are a few points to remember regarding image segmentation: Medical Segmentation is the most significant use case. Most segmentation models discussed above aim to segment complex medical images to detect anomalies. Few-shot Learning: Few-shot learning methods make it easier for experts to develop models for segmenting novel images. Transformer-based Architectures: The transformer architecture is becoming a popular framework for segmentation tasks due to its simplicity and higher processing speeds than traditional methods.
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