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.
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.
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.
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.
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.
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.
Power the next generation of LLMs & VLMs with Reinforcement Learning from Human Feedback
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
Build Better Models, Faster with Encord's Leading Annotation Tool
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.
Written by Nikolaj Buhl
Nikolaj is a Product Manager at Encord and a computer vision enthusiast. At Encord he oversees the development of Encord Active. Nikolaj holds a M.Sc. in Management from London Business School and Copenhagen Business School. In a previous life, he lived in China working at the Danish Embas... see more
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.
With image and video data fueling advancements across various industries, the video and image annotation tool market is witnessing rapid expansion, projected to grow at a compound annual growth rate (CAGR) of 30% between 2023 and 2032. This growth is particularly pronounced in autonomous vehicles, healthcare, and retail sectors, where precise and accurate data annotation is crucial. The increased demand for these tools results from the need to develop robust quality assurance processes, integrate automation for efficiency, collaborate features for team-based annotation, and streamline labeling workflows to produce high-quality training data. However, the extensive choice of annotation tools makes choosing a suitable platform that suits your requirements challenging. There are a plethora of available options, each with varying features, scalability, and pricing models. This article will guide you through this tooling landscape. It highlights five critical questions you must ask before investing in a video annotation tool to ensure it aligns with your project requirements and goals. Key Factors that Hinder Efficient Annotation Project Management A robust video annotation tool helps improve annotation workflows, but selecting an appropriate solution requires you to: Consider the tool’s ability to render videos natively Track objects using advanced algorithms Perform frame-by-frame analysis Doing all those while determining its scalability, quality, integrability, and cost to guide your choice. Below are a few factors that can be potential bottlenecks to your CV project. Native Video Rendering Annotating long-form videos can be challenging if the annotation tool lacks features for rendering videos natively. The operative costs can be prohibitive if you use external tools to render multiple videos, limiting your budget for the annotation project. Object Tracking and Frame-by-Frame Analysis Another obstacle to video annotation is sub-optimal object tracking algorithms that cannot address occlusion, camera shift, and image blur. Traditional tracking algorithms use a detection framework to identify objects within separate video frames. However, detecting and tracking objects frame-by-frame can cause annotation inconsistency and increase data transfer volume. If you are using a cloud platform that charges based on data usage, this will result in inaccurate labels, processing delays, and high storage costs. Scalability Handling large and complex video data is essential for providing a high-quality user experience. However, maintaining quality requires error-free training data with accurate labels to build robust computer vision models that can efficiently process video feeds. Finding a tool that you can quickly scale to rising demands is difficult due to the constantly evolving data landscape. Tools with limited scalability can soon become a bottleneck as you start labeling extensive datasets for training large-scale CV applications. For instance, the pipelines can break as you feed more data. This can result in missed deadlines, deployment delays, and budgetary runs as you hire more annotators to compensate for the tool’s shortcomings. Quality of Annotation Annotation quality directly affects the performance of supervised learning models, which rely heavily on accurately labeled data for training. Consider developing a machine learning model for a surveillance system to detect abnormal behavior and alert relevant authorities to prevent accidents. If the model’s training set included video feeds with erroneous labels, it could not efficiently recognize security threats. This would result in false alarms and missed targets, which would lead to adverse security incidents. Deploying such models in crowded areas can be more detrimental, as the system will not flag suspicious actions in time. Mitigating these problems requires the annotation tool to have quality assurance and collaboration features, which will help human annotators verify labeling accuracy and fix errors proactively. Integrability with Existing Infrastructure Developing robust artificial intelligence (AI) models requires more than the best algorithms and evaluation strategies. Instead, the emphasis should be on an integrated infrastructure that seamlessly handles data collection, storage, preprocessing, and curation. As annotation is a vital element of a data curation pipeline, a tool that quickly integrates with your existing machinery can significantly boost productivity and quality. Businesses that fail to build an integrated system operate multiple disparate systems without synchronization. This results in increased manual effort to organize data assets, which can lead to suboptimal workflows and poor deployment procedures. Cost A data annotation tool that provides flexible pricing options to upgrade or downgrade your plans according to project needs makes financing decisions easier, paving the way for a faster return on investment (ROI). A cost-effective tool helps with executive buy-in as it becomes easier for the management to convince the executive team to undertake innovative projects and continue the development process without budgetary hurdles. Learn how to automate video annotation by reading our guide on video annotation automation. How to Select a Video Annotation Tool Due to the challenges discussed above, choosing a tool that meets your required standards becomes time-consuming and delays the launch of your CV application. So, the following sections explain the primary factors you should consider when investing in a labeling platform. They will help you quickly filter out the desired features to speed up your annotation processes. What are Your Annotation Needs? Understanding the exact annotation requirements should be the first step in selecting a tool, and the following factors must be included: The Type of Computer Vision (CV) Application CV models for applications like autonomous driving and real-time surveillance call for a scalable annotation platform to label large amounts of real-time video feeds. The type of application will also determine what category of annotation is necessary and whether a particular tool offers the required functionality. Critical applications like medical imaging require pixel-level segmentation masks, while bounding boxes will suffice for security surveillance. Automation for Video-specific Complexities Videos with higher frames-per-second (FPS) can take longer to label since annotators must classify objects within each frame. Additionally, videos with higher motion speeds can cause blurred-out frames or motion blur. This is especially true for action recognition CV models, where labeling frequently changing human actions becomes challenging. The solution to these issues is to have tools with automated labeling techniques that use pre-trained models (AI-assisted annotations) to label samples in real time using data pipelines with interpolation algorithms to fix blurry frames. Platform Compatibility and User Interface (UI) A tool compatible with several operating systems and environments can improve integrability and prevent disruptions to annotation projects. Similarly, the tool’s UI must be intuitive so annotators can quickly learn to use the platform, reducing the time required for staff training. Video Format Compatibility For optimal data processing, annotation tools must support multiple video formats, such as MP4, AVI, FLV, etc., and provide features to convert annotations into suitable formats to train CV models quickly. Video Annotation Tool: Must-have Functionalities Based on the above considerations, a video annotation tool must have: Features to natively label video datasets frame-by-frame for advanced object tracking so that minimal downsampling is required. There are basic types of annotations, such as keypoint annotation for pose estimation, 2D bounding boxes, cuboids, polylines, and polygons for labeling objects within a single video frame. Advanced annotation techniques include semantic segmentation, object tracking algorithms, and temporal annotation. Suitable APIs and SDKs can be used to integrate with existing data pipelines programmatically. While these factors are essential for a video annotation tool, it is also advisable to have a manual review process to assess annotation accuracy for high-precision tasks, such as medical imaging, surgical videos, and autonomous navigations. Encord Annotate addresses all the above concerns by offering scalable features and algorithms to handle project complexities, extensive labeling techniques, and automation to speed up the annotation process. How Do You Evaluate Annotation Efficiency? The annotation tool should allow you to compute annotation speed and accuracy through intuitive metrics that reflect actual annotation performance. The list below mentions a few popular metrics for measuring the two factors. Metrics for Measuring Annotation Speed Annotations per hour: Determine the 'annotations per hour' to gauge productivity, contextualizing it with industry norms or project expectations. Frames per minute: Evaluate 'frames per minute' to understand annotator performance in video contexts, considering the video complexity. Time per annotation: Use 'time per annotation' to assess individual annotation task efficiency, adjusting expectations based on the required annotation detail. Metrics for Measuring Annotation Accuracy F1-score: Use the F1-score to balance precision and recall scores, explaining its calculation through Intersection over Union (IoU) in video contexts—IoU determines precision and recall in video frames. Cohen’s Kappa and Fleiss’ Kappa: Use Cohen's Kappa and Fleiss’ Kappa for annotator agreement analysis, providing context for when each is most applicable. Krippendorff’s Alpha: Consider Krippendorff’s alpha for diverse or incomplete datasets, detailing its significance in ensuring consistent annotation quality. Ability to Process Complex Annotation Scenarios Ensure the tool can effectively manage challenges like object occlusion, multiple object tracking, and variable backgrounds. Provide examples to illustrate how these are addressed. Discuss the tool's adaptability to different annotation complexities and how its features facilitate accurate labeling in varied scenarios. Customization and Integrations Customization and integrability with ML models are valuable capabilities that can help you tailor a tool’s annotation features to address use-case-specific needs. Know if they allow you to use open-source annotation libraries to improve existing functionality. Encord Annotate offers multiple quality metrics to analyze annotation quality and ensures high efficiency that meets current industry standards. How Flexible do you Want the Features to be? While the features mentioned above directly relate to annotation functionality, video annotation software must have other advanced tools to streamline the process for computer vision projects. These include tools for managing ontology, handling long-form video footage, quality control, and AI-based labeling. Ontology Management Ontologies are high-level concepts that specify what and how to label and whether additional information is necessary for model training. Users can define hierarchical structures to relate multiple concepts and create a richer annotated dataset for training CV models. For instance, an ontology for autonomous driving applications specifies that the labeler must annotate a car with 2D bounding boxes and provide information about its model, color, type, etc. These ontologies allow annotators to correctly identify objects of interest in complex videos and include additional information relevant to scene understanding. Clarifying how users can adapt these ontologies across various project types demonstrates the tool's adaptability to diverse research and industry needs. Features to Manage Long-form Videos Long-form videos pose unique challenges, as annotators must track longer video sequences and manage labels in more frames. Suitable tools that allow you to move back and forth between frames and timelines simplify video analysis. You can easily navigate through the footage to examine objects and scenes. Segmentation: Segmentation is also a valuable feature to look out for, as it allows you to break long videos into smaller segments to create manageable annotation tasks. For instance, automated checks that monitor labels across segments help you identify discrepancies and ensure identical objects have consistent labeling within each segment. Version Control: Finally, version control features let you save and reload previous annotation work, helping you track your progress and synchronize tasks across multiple annotators. Tools that allow annotators to store annotation revision history and tag particular versions help maintain a clear audit trail. These functionalities improve user experience by reducing fatigue and mitigating errors, as annotators can label long-form videos in separate stages. It also helps with quick recovery in case a particular version becomes corrupt. Customizable Workflows and Performance Monitoring Annotation tools that let you customize workflows and guidelines based on project requirements can improve annotation speed by removing redundancies and building processes that match existing annotators’ expertise. Further, intuitive dashboards that display relevant performance metrics regarding annotation progress and quality can allow management to track issues and make data-driven decisions to boost operational efficiency. Inter-annotator agreement (IAA), annotation speed, and feedback metrics that signify revision cycles are most useful in monitoring annotation efficiency. For instance, an increasing number of revisions denotes inconsistencies and calls for a root-cause analysis to identify fundamental issues. AI-assisted Labeling AI-assisted labeling that involves developing models for domain-specific annotation tasks can be costly, as the process requires manual effort to label sufficient samples for pre-training the labeling algorithms. An alternative approach is using techniques like interpolation, semantic and instance segmentation, object tracking, and detection to label video frames without developing a custom model. For example, video annotation tools with object-tracking algorithms can automatically identify objects of interest and fill in the gaps using only a small set of manually labeled data. The method enhances annotation efficiency as annotators do not have to train a separate model from scratch and only label a few items while leaving the rest for AI. Quality Assurance and Access Control Regardless of the level of automation, labeling is error-prone, as it is challenging to annotate each object in all video frames correctly. This limitation requires a tool with quality assurance features, such as feedback cycles, progress trackers, and commenting protocols. These features help human annotators collaborate with experts to identify and fix errors. Efficient access control features also become crucial for managing access across different teams and assigning relevant roles to multiple members within a project. The Encord platform features robust AI-based annotation algorithms, allowing you to integrate custom models, build tailored workflows, and create detailed ontologies to manage long-form videos. What Type of Vendor Are You Looking for? The next vital step in evaluating a tool is assessing different vendors and comparing their annotation services and platforms against standard benchmarks while factoring in upfront and ongoing costs. A straightforward strategy is to list the required features for your annotation project and draw a comparison table to determine which platforms offer these features and at what cost. Here are a few points you should address: Managed Service vs. Standalone Platform: You must see whether you require a managed service or a standalone application. While a managed service frees you from annotating the data in-house, a standalone tool offers more security and transparency in the annotation process. A side-by-side comparison detailing each model's implications on your workflow and data governance practices can guide your decision. Onboarding Costs: Analyze all costs associated with adopting and using the tool, distinguishing between one-time onboarding fees, recurring licensing costs, and any potential hidden fees. Consider creating a multi-year cost projection to understand the total cost of ownership and how it compares to the projected ROI. Ecosystem Strength: A vendor with a robust community and ecosystem offers additional resources to maximize the value of your tool investment, including access to a broader range of insights, support, and potential integrations. Long-term Suitability: Other relevant factors in evaluating vendors include customer reviews, vendor’s track record in providing regular updates, supporting innovative projects, long-term clients, and customer support quality. Analyzing these will help you assess whether the vendor is a suitable long-run strategic partner who will proactively support your company’s mission and vision. What is the Standard of Post-purchase Services Investing in a video annotation tool is a long-term strategic action involving repeated interactions with the vendor to ensure a smooth transition process and continuous improvements. Below are a few essential services that vendors must offer post-purchase to provide greater value and meet changing demands as per project requirements. Training Resources: The vendor must provide easy access to relevant training materials, such as detailed documentation, video tutorials, and on-site support, to help users fully utilize the tool’s feature set from the start. Data Security Protocols: While compliance with established security standards, including GDPR, HIPAA, ISO, and SOC, is crucial, the vendor must continuously update its encryption protocols to address the dynamic nature of data and rising privacy concerns. Post-purchase, the vendor must ensure robust security measures by following ethical practices and analyzing sensitive information in your project to implement suitable safeguards to prevent breaches and data misuse. Customer Support: The vendor must offer 24/7 customer support helplines for bug resolution and workflow assistance. Want to know the most crucial features of a video annotation tool? Read our article on the five features of video annotation. Encord complies with HIPAA, FDA, and CE standards, making it an ideal tool for sensitive annotation tasks, especially for medical use cases. Evaluating a Video Annotation Tool: Key Takeaways As CV models permeate multiple domains, such as healthcare, retail, and manufacturing, video annotation tools will be critical determinants of the success of modern CV projects. Below are a few key factors you should consider when evaluating a video annotation platform. Annotation Requirements: The answer will allow you to filter out the desired feature set and scalability demands. Evaluation of Annotation Efficiency: Understanding evaluation methodologies will help you select a tool that offers suitable metrics to assess annotation speed and accuracy. Feature Flexibility: Ontology management, AI-assisted labeling, and options to customize workflows are crucial features that allow you to tailor the tool’s feature set to your requirements. Strategic Vendor Evaluation: Analyzing upfront and ongoing costs helps you determine the total cost of ownership and whether the vendor is a suitable long-term strategic partner. Quality of Post-purchase Services: With the ever-changing data landscape, you need a vendor that constantly updates its security and training protocols to keep pace with ongoing developments.
With image and video data fueling advancements across various industries, the video and image annotation tool market is witnessing rapid expansion, projected to grow at a compound annual growth rate (CAGR) of 30% between 2023 and 2032. This growth is particularly pronounced in autonomous vehicles, healthcare, and retail sectors, where precise and accurate data annotation is crucial. The increased demand for these tools results from the need to develop robust quality assurance processes, integrate automation for efficiency, collaborate features for team-based annotation, and streamline labeling workflows to produce high-quality training data. However, the extensive choice of annotation tools makes choosing a suitable platform that suits your requirements challenging. There are a plethora of available options, each with varying features, scalability, and pricing models. This article will guide you through this tooling landscape. It highlights five critical questions you must ask before investing in a video annotation tool to ensure it aligns with your project requirements and goals. Key Factors that Hinder Efficient Annotation Project Management A robust video annotation tool helps improve annotation workflows, but selecting an appropriate solution requires you to: Consider the tool’s ability to render videos natively Track objects using advanced algorithms Perform frame-by-frame analysis Doing all those while determining its scalability, quality, integrability, and cost to guide your choice. Below are a few factors that can be potential bottlenecks to your CV project. Native Video Rendering Annotating long-form videos can be challenging if the annotation tool lacks features for rendering videos natively. The operative costs can be prohibitive if you use external tools to render multiple videos, limiting your budget for the annotation project. Object Tracking and Frame-by-Frame Analysis Another obstacle to video annotation is sub-optimal object tracking algorithms that cannot address occlusion, camera shift, and image blur. Traditional tracking algorithms use a detection framework to identify objects within separate video frames. However, detecting and tracking objects frame-by-frame can cause annotation inconsistency and increase data transfer volume. If you are using a cloud platform that charges based on data usage, this will result in inaccurate labels, processing delays, and high storage costs. Scalability Handling large and complex video data is essential for providing a high-quality user experience. However, maintaining quality requires error-free training data with accurate labels to build robust computer vision models that can efficiently process video feeds. Finding a tool that you can quickly scale to rising demands is difficult due to the constantly evolving data landscape. Tools with limited scalability can soon become a bottleneck as you start labeling extensive datasets for training large-scale CV applications. For instance, the pipelines can break as you feed more data. This can result in missed deadlines, deployment delays, and budgetary runs as you hire more annotators to compensate for the tool’s shortcomings. Quality of Annotation Annotation quality directly affects the performance of supervised learning models, which rely heavily on accurately labeled data for training. Consider developing a machine learning model for a surveillance system to detect abnormal behavior and alert relevant authorities to prevent accidents. If the model’s training set included video feeds with erroneous labels, it could not efficiently recognize security threats. This would result in false alarms and missed targets, which would lead to adverse security incidents. Deploying such models in crowded areas can be more detrimental, as the system will not flag suspicious actions in time. Mitigating these problems requires the annotation tool to have quality assurance and collaboration features, which will help human annotators verify labeling accuracy and fix errors proactively. Integrability with Existing Infrastructure Developing robust artificial intelligence (AI) models requires more than the best algorithms and evaluation strategies. Instead, the emphasis should be on an integrated infrastructure that seamlessly handles data collection, storage, preprocessing, and curation. As annotation is a vital element of a data curation pipeline, a tool that quickly integrates with your existing machinery can significantly boost productivity and quality. Businesses that fail to build an integrated system operate multiple disparate systems without synchronization. This results in increased manual effort to organize data assets, which can lead to suboptimal workflows and poor deployment procedures. Cost A data annotation tool that provides flexible pricing options to upgrade or downgrade your plans according to project needs makes financing decisions easier, paving the way for a faster return on investment (ROI). A cost-effective tool helps with executive buy-in as it becomes easier for the management to convince the executive team to undertake innovative projects and continue the development process without budgetary hurdles. Learn how to automate video annotation by reading our guide on video annotation automation. How to Select a Video Annotation Tool Due to the challenges discussed above, choosing a tool that meets your required standards becomes time-consuming and delays the launch of your CV application. So, the following sections explain the primary factors you should consider when investing in a labeling platform. They will help you quickly filter out the desired features to speed up your annotation processes. What are Your Annotation Needs? Understanding the exact annotation requirements should be the first step in selecting a tool, and the following factors must be included: The Type of Computer Vision (CV) Application CV models for applications like autonomous driving and real-time surveillance call for a scalable annotation platform to label large amounts of real-time video feeds. The type of application will also determine what category of annotation is necessary and whether a particular tool offers the required functionality. Critical applications like medical imaging require pixel-level segmentation masks, while bounding boxes will suffice for security surveillance. Automation for Video-specific Complexities Videos with higher frames-per-second (FPS) can take longer to label since annotators must classify objects within each frame. Additionally, videos with higher motion speeds can cause blurred-out frames or motion blur. This is especially true for action recognition CV models, where labeling frequently changing human actions becomes challenging. The solution to these issues is to have tools with automated labeling techniques that use pre-trained models (AI-assisted annotations) to label samples in real time using data pipelines with interpolation algorithms to fix blurry frames. Platform Compatibility and User Interface (UI) A tool compatible with several operating systems and environments can improve integrability and prevent disruptions to annotation projects. Similarly, the tool’s UI must be intuitive so annotators can quickly learn to use the platform, reducing the time required for staff training. Video Format Compatibility For optimal data processing, annotation tools must support multiple video formats, such as MP4, AVI, FLV, etc., and provide features to convert annotations into suitable formats to train CV models quickly. Video Annotation Tool: Must-have Functionalities Based on the above considerations, a video annotation tool must have: Features to natively label video datasets frame-by-frame for advanced object tracking so that minimal downsampling is required. There are basic types of annotations, such as keypoint annotation for pose estimation, 2D bounding boxes, cuboids, polylines, and polygons for labeling objects within a single video frame. Advanced annotation techniques include semantic segmentation, object tracking algorithms, and temporal annotation. Suitable APIs and SDKs can be used to integrate with existing data pipelines programmatically. While these factors are essential for a video annotation tool, it is also advisable to have a manual review process to assess annotation accuracy for high-precision tasks, such as medical imaging, surgical videos, and autonomous navigations. Encord Annotate addresses all the above concerns by offering scalable features and algorithms to handle project complexities, extensive labeling techniques, and automation to speed up the annotation process. How Do You Evaluate Annotation Efficiency? The annotation tool should allow you to compute annotation speed and accuracy through intuitive metrics that reflect actual annotation performance. The list below mentions a few popular metrics for measuring the two factors. Metrics for Measuring Annotation Speed Annotations per hour: Determine the 'annotations per hour' to gauge productivity, contextualizing it with industry norms or project expectations. Frames per minute: Evaluate 'frames per minute' to understand annotator performance in video contexts, considering the video complexity. Time per annotation: Use 'time per annotation' to assess individual annotation task efficiency, adjusting expectations based on the required annotation detail. Metrics for Measuring Annotation Accuracy F1-score: Use the F1-score to balance precision and recall scores, explaining its calculation through Intersection over Union (IoU) in video contexts—IoU determines precision and recall in video frames. Cohen’s Kappa and Fleiss’ Kappa: Use Cohen's Kappa and Fleiss’ Kappa for annotator agreement analysis, providing context for when each is most applicable. Krippendorff’s Alpha: Consider Krippendorff’s alpha for diverse or incomplete datasets, detailing its significance in ensuring consistent annotation quality. Ability to Process Complex Annotation Scenarios Ensure the tool can effectively manage challenges like object occlusion, multiple object tracking, and variable backgrounds. Provide examples to illustrate how these are addressed. Discuss the tool's adaptability to different annotation complexities and how its features facilitate accurate labeling in varied scenarios. Customization and Integrations Customization and integrability with ML models are valuable capabilities that can help you tailor a tool’s annotation features to address use-case-specific needs. Know if they allow you to use open-source annotation libraries to improve existing functionality. Encord Annotate offers multiple quality metrics to analyze annotation quality and ensures high efficiency that meets current industry standards. How Flexible do you Want the Features to be? While the features mentioned above directly relate to annotation functionality, video annotation software must have other advanced tools to streamline the process for computer vision projects. These include tools for managing ontology, handling long-form video footage, quality control, and AI-based labeling. Ontology Management Ontologies are high-level concepts that specify what and how to label and whether additional information is necessary for model training. Users can define hierarchical structures to relate multiple concepts and create a richer annotated dataset for training CV models. For instance, an ontology for autonomous driving applications specifies that the labeler must annotate a car with 2D bounding boxes and provide information about its model, color, type, etc. These ontologies allow annotators to correctly identify objects of interest in complex videos and include additional information relevant to scene understanding. Clarifying how users can adapt these ontologies across various project types demonstrates the tool's adaptability to diverse research and industry needs. Features to Manage Long-form Videos Long-form videos pose unique challenges, as annotators must track longer video sequences and manage labels in more frames. Suitable tools that allow you to move back and forth between frames and timelines simplify video analysis. You can easily navigate through the footage to examine objects and scenes. Segmentation: Segmentation is also a valuable feature to look out for, as it allows you to break long videos into smaller segments to create manageable annotation tasks. For instance, automated checks that monitor labels across segments help you identify discrepancies and ensure identical objects have consistent labeling within each segment. Version Control: Finally, version control features let you save and reload previous annotation work, helping you track your progress and synchronize tasks across multiple annotators. Tools that allow annotators to store annotation revision history and tag particular versions help maintain a clear audit trail. These functionalities improve user experience by reducing fatigue and mitigating errors, as annotators can label long-form videos in separate stages. It also helps with quick recovery in case a particular version becomes corrupt. Customizable Workflows and Performance Monitoring Annotation tools that let you customize workflows and guidelines based on project requirements can improve annotation speed by removing redundancies and building processes that match existing annotators’ expertise. Further, intuitive dashboards that display relevant performance metrics regarding annotation progress and quality can allow management to track issues and make data-driven decisions to boost operational efficiency. Inter-annotator agreement (IAA), annotation speed, and feedback metrics that signify revision cycles are most useful in monitoring annotation efficiency. For instance, an increasing number of revisions denotes inconsistencies and calls for a root-cause analysis to identify fundamental issues. AI-assisted Labeling AI-assisted labeling that involves developing models for domain-specific annotation tasks can be costly, as the process requires manual effort to label sufficient samples for pre-training the labeling algorithms. An alternative approach is using techniques like interpolation, semantic and instance segmentation, object tracking, and detection to label video frames without developing a custom model. For example, video annotation tools with object-tracking algorithms can automatically identify objects of interest and fill in the gaps using only a small set of manually labeled data. The method enhances annotation efficiency as annotators do not have to train a separate model from scratch and only label a few items while leaving the rest for AI. Quality Assurance and Access Control Regardless of the level of automation, labeling is error-prone, as it is challenging to annotate each object in all video frames correctly. This limitation requires a tool with quality assurance features, such as feedback cycles, progress trackers, and commenting protocols. These features help human annotators collaborate with experts to identify and fix errors. Efficient access control features also become crucial for managing access across different teams and assigning relevant roles to multiple members within a project. The Encord platform features robust AI-based annotation algorithms, allowing you to integrate custom models, build tailored workflows, and create detailed ontologies to manage long-form videos. What Type of Vendor Are You Looking for? The next vital step in evaluating a tool is assessing different vendors and comparing their annotation services and platforms against standard benchmarks while factoring in upfront and ongoing costs. A straightforward strategy is to list the required features for your annotation project and draw a comparison table to determine which platforms offer these features and at what cost. Here are a few points you should address: Managed Service vs. Standalone Platform: You must see whether you require a managed service or a standalone application. While a managed service frees you from annotating the data in-house, a standalone tool offers more security and transparency in the annotation process. A side-by-side comparison detailing each model's implications on your workflow and data governance practices can guide your decision. Onboarding Costs: Analyze all costs associated with adopting and using the tool, distinguishing between one-time onboarding fees, recurring licensing costs, and any potential hidden fees. Consider creating a multi-year cost projection to understand the total cost of ownership and how it compares to the projected ROI. Ecosystem Strength: A vendor with a robust community and ecosystem offers additional resources to maximize the value of your tool investment, including access to a broader range of insights, support, and potential integrations. Long-term Suitability: Other relevant factors in evaluating vendors include customer reviews, vendor’s track record in providing regular updates, supporting innovative projects, long-term clients, and customer support quality. Analyzing these will help you assess whether the vendor is a suitable long-run strategic partner who will proactively support your company’s mission and vision. What is the Standard of Post-purchase Services Investing in a video annotation tool is a long-term strategic action involving repeated interactions with the vendor to ensure a smooth transition process and continuous improvements. Below are a few essential services that vendors must offer post-purchase to provide greater value and meet changing demands as per project requirements. Training Resources: The vendor must provide easy access to relevant training materials, such as detailed documentation, video tutorials, and on-site support, to help users fully utilize the tool’s feature set from the start. Data Security Protocols: While compliance with established security standards, including GDPR, HIPAA, ISO, and SOC, is crucial, the vendor must continuously update its encryption protocols to address the dynamic nature of data and rising privacy concerns. Post-purchase, the vendor must ensure robust security measures by following ethical practices and analyzing sensitive information in your project to implement suitable safeguards to prevent breaches and data misuse. Customer Support: The vendor must offer 24/7 customer support helplines for bug resolution and workflow assistance. Want to know the most crucial features of a video annotation tool? Read our article on the five features of video annotation. Encord complies with HIPAA, FDA, and CE standards, making it an ideal tool for sensitive annotation tasks, especially for medical use cases. Evaluating a Video Annotation Tool: Key Takeaways As CV models permeate multiple domains, such as healthcare, retail, and manufacturing, video annotation tools will be critical determinants of the success of modern CV projects. Below are a few key factors you should consider when evaluating a video annotation platform. Annotation Requirements: The answer will allow you to filter out the desired feature set and scalability demands. Evaluation of Annotation Efficiency: Understanding evaluation methodologies will help you select a tool that offers suitable metrics to assess annotation speed and accuracy. Feature Flexibility: Ontology management, AI-assisted labeling, and options to customize workflows are crucial features that allow you to tailor the tool’s feature set to your requirements. Strategic Vendor Evaluation: Analyzing upfront and ongoing costs helps you determine the total cost of ownership and whether the vendor is a suitable long-term strategic partner. Quality of Post-purchase Services: With the ever-changing data landscape, you need a vendor that constantly updates its security and training protocols to keep pace with ongoing developments.
March 8
8 min
Frequently asked questions
Some good open source libraries for Computer Vision include OpenCV, Awesome Computer Vision, LearnOpenCV, Papers with Code, Microsoft CV Recipes on GitHub, Visual Transformer, Segment Anything by Facebook on GitHub, and many more. These libraries offer a wide range of tools and resources for developing Computer Vision applications and conducting research in the field. Additionally, exploring GitHub repositories dedicated to Computer Vision projects can also provide valuable insights and resources for developers looking to contribute to the community.
Yes, several free data repositories are available for Computer Vision and image understanding projects, such as ImageNet, COCO, and CIFAR-10. These repositories provide large datasets that can be used for training and testing Computer Vision algorithms and models. Platforms like Kaggle also offer access to various datasets and competitions related to Computer Vision tasks.
One way to quickly search for classic papers in your area of interest is to use academic search engines like Google Scholar or IEEE Xplore. These platforms allow you to filter search results by relevance, publication date, and citations, making finding influential papers in the field easier. Additionally, joining online communities or forums related to Computer Vision and image understanding can also help you discover recommended readings and resources from experts in the field.
OpenCV is a popular project on GitHub for image processing and Computer Vision. It offers a wide range of tools and libraries for various applications. Another interesting project is TensorFlow's Object Detection API, which provides pre-trained models for object detection tasks in images and videos.
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