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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 and SAM.

notebook	open in colab / kaggle / sagemaker studio lab	complementary materials	repository / paper
Fast Segment Anything Model (FastSAM)
YOLO-NAS Object Detection
Segment Anything Model (SAM)
Zero-Shot Object Detection with Grounding DINO
DETR Transformer Object Detection
DINOv2 Image Classification
YOLOv8 Object Detection
YOLOv8 Instance Segmentation
YOLOv8 Classification
YOLOv7 Object Detection
YOLOv7 Instance Segmentation
YOLOv7 Object Detection OpenVINO + TorchORT
MT-YOLOv6 Object Detection
YOLOv5 Object Detection
YOLOv5 Classification
YOLOv5 Instance Segmentation
Detection2 Instance Segmentation
SegFormer Instance Segmentation
Vision Transformer Classification
Scaled-YOLOv4 Object Detection
YOLOS Object Detection
YOLOR Object Detection
YOLOX Object Detection
Resnet34 fast.ai Classification
OpenAI Clip Classification
YOLOv4-tiny Darknet Object Detection

notebook	open in colab / kaggle / sagemaker studio lab	complementary materials	repository / paper
Auto Train YOLOv8 Model with Autodistill
DINOv2 Image Retrieval
Image Embeddings Analysis - Part 1
Automated Dataset Annotation and Evaluation with Grounding DINO and SAM
Automated Dataset Annotation and Evaluation with Grounding DINO
Vector Analysis with Scikit-learn and Bokeh
RF100 Object Detection Model Benchmarking
Detect and Count Objects in Polygon Zone with YOLOv5 / YOLOv8 / Detectron2 + Supervision
Track and Count Vehicles with YOLOv8 + ByteTRACK + Supervision
Football Players Tracking with YOLOv5 + ByteTRACK
Create Segmentation Masks with Roboflow
How to Use PolygonZone and Roboflow Supervision

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!

How to Choose the Best Computer Vision Model for Your Project

Accelerate Image Annotation with SAM and Grounding DINO

SAM - Segment Anything Model by Meta AI: Complete Guide

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...

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 [email protected]: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

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

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.