10 GitHub Repositories to Master Computer Vision

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Computer vision is a rapidly growing field that enables machines to interpret and understand visual data. It is gaining popularity due to image generation models like Stable Dignity and Flux.1, as well as the multimodal GPT-4o Vision model that enables large language models to understand images. Therefore, computer vision is at the forefront of the AI race, and it is the perfect time to start learning it.

In this blog, we will explore ten essential GitHub repositories that offer comprehensive learning resources, research papers, guides, popular tools, tutorials, projects, and datasets to improve your computer vision skills.

 

1. Awesome Computer Vision

 

Link: jbhuang0604/awesome-computer-vision

It is a curated list of resources that includes links to books, papers, software, datasets, pre-trained models, tutorials, and more. This repository contains everything you need to start your computer vision journey. The best part of the repository is that it provides additional links to an awesome list that will help you delve deep into computer vision specialties like Deep Vision, Object Detection, Face Recognition, and more.

 

2. Awesome Deep Vision

 

Link: kjw0612/awesome-deep-vision

The GitHub repository provides a curated list of deep learning resources specifically for computer vision. It includes a comprehensive collection of papers, datasets, books, tutorials, and courses, making it an invaluable resource for those interested in learning deep computer vision. Furthermore, it is divided into subtopics such as ImageNet Classification, Object Detection, Object Tracking, Low-Level Vision, and more.

 

3. Awesome Object Detection

 

Link: amusi/awesome-object-detection

It focuses on object detection and provides a comprehensive list of resources, including papers, datasets, software, projects, and tutorials. The GitHub repository is perfect for those looking to specialize in object detection techniques and applications, especially if they are interested in R-CNN, YOLO, ResNet, and other computer vision models.

 

4. 3D Machine Learning

 

Link: timzhang642/3D-Machine-Learning

This repository provides access to papers, datasets, software, projects, and tutorials for individuals interested in 3D data and its applications in computer vision. It is an ideal resource for exploring the intricacies of 3D machine learning, and learning about 3D Pose Estimation, 3D Geometry Synthesis/Reconstruction, Style Learning and Transfer, and Scene Understanding.

 

5. Medical Imaging Datasets

 

Link: sfikas/medical-imaging-datasets

Computer vision has many applications in medicine, and for that, you require a high-quality dataset without any licensing issues. This repository provides a list of medical imaging datasets, particularly useful for researchers and developers working in the field of medical imaging.

 

6. 500 AI Machine Learning Projects

 

Link: ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

It contains 500 AI, machine learning, deep learning, computer vision, and NLP projects with code. This repository provides hands-on learning opportunities and is perfect for those looking to practice and implement various projects. The thing is that you have to find the link to the computer vision projects and scroll down. Not all of them are computer vision projects.

 

7. Deep Learning Drizzle

 

Link: kmario23/deep-learning-drizzle

The GitHub repository offers lectures on deep learning, reinforcement learning, machine learning, computer vision, and NLP from top universities in the world, such as Oxford, the University of Toronto, and Stanford University. It provides free top-tier education for individuals who cannot enroll due to financial or geographical restrictions.

 

8. Computer Vision Recipes

 

Link: microsoft/computervision-recipes

The repository provides best practices, code samples, and documentation for computer vision. It offers practical insights and guidance, making it a valuable resource for building and optimizing computer vision applications. You will be working on classification, image similarity, object detection, image segmentation, action recognition, tracking, and crowd-counting projects.

 

9. Roboflow Notebooks

 

Link: roboflow/notebooks

It contains examples and tutorials on using state-of-the-art computer vision models and techniques. The reposiotry covers a wide range of models from old-school ResNet to the latest advancements like Grounding DINO and SAM, making it a comprehensive resource for learning and experimenting with various computer vision models.

 

Note: The code resources are available in Google Colab Notebooks, Kaggle Notebooks, and sometimes SageMaker Studio Lab. Each project includes supplementary materials such as blogs, YouTube videos, model reports, and papers.

 

10. Awesome Vision and Language

 

Link: awesome-vision-language

This repository is a compilation of resources for vision and language research. It contains datasets, pre-trained models, sample code, and research papers, providing a comprehensive guide to the vision and language field, which is increasingly crucial in modern AI applications. The repository covers topics such as Image Captioning, Image Retrieval, Scene Text Recognition (OCR), Scene Graph, text2image, and Video Captioning.

 

Conclusion

 

The ten GitHub repositories mentioned offer a wealth of resources for mastering computer vision. With such a vast field, success lies in choosing a niche and immersing yourself. Discover your interests by engaging with tutorials, courses, projects, fine-tuning models, and documenting your journey. These repositories will provide the necessary resources and tools, saving you valuable time.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.