Grok-1.5 Vision: First Multimodal Model from Elon Musk’s xAI (2024)

Computer Vision

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.

Computer Vision

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.

Computer Vision

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.