Over the years we have created dozens of Computer Vision tutorials. This repository contains examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO, SAM, and GPT-4 Vision.
Curious to learn more about GPT-4 Vision? Check out our GPT-4V experiments 🧪 repository.
🚀 model tutorials (28 notebooks)
📸 computer vision skills (17 notebooks)
Almost every week we create tutorials showing you the hottest models in Computer Vision. 🔥 Subscribe, and stay up to date with our latest YouTube videos!
In this video, we will dive into the complexity of choosing the right computer vision model for your unique project. From the importance of high-quality datasets to hardware considerations, interoperability, benchmarking, and licensing issues, this video covers it all...
Discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model (SAM). Learn how to convert object detection datasets into instance segmentation datasets, and see the potential of using these models to automatically annotate your datasets for real-time detectors like YOLOv8...
Discover the incredible potential of Meta AI's Segment Anything Model (SAM)! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often superior to prior fully supervised results...
💻 run locally
We try to make it as easy as possible to run Roboflow Notebooks in Colab and Kaggle, but if you still want to run them locally, below you will find instructions on how to do it. Remember don't install your dependencies globally, use venv.
# clone repository and navigate to root directory git clone firstname.lastname@example.org:roboflow-ai/notebooks.git cd notebooks # setup python environment and activate it python3 -m venv venv source venv/bin/activate # install and run jupyter notebook pip install notebook jupyter notebook
☁️ run in sagemaker studio lab
You can now open our tutorial notebooks in Amazon SageMaker Studio Lab - a free machine learning development environment that provides the compute, storage, and security—all at no cost—for anyone to learn and experiment with ML.
|Stable Diffusion Image Generation||YOLOv5 Custom Dataset Training||YOLOv7 Custom Dataset Training|
🐞 bugs & 🦸 contribution
Computer Vision moves fast! Sometimes our notebooks lag a tad behind the ever-pushing forward libraries. If you notice that any of the notebooks is not working properly, create a bug report and let us know.
If you have an idea for a new tutorial we should do, create a feature request. We are constantly looking for new ideas. If you feel up to the task and want to create a tutorial yourself, please take a peek at our contribution guide. There you can find all the information you need.
We are here for you, so don't hesitate to reach out.