notebooks | inference | autodistill | collect

👋 hello

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 (32 notebooks)

notebook	open in colab / kaggle / sagemaker studio lab	complementary materials	repository / paper
Zero-Shot Object Detection with YOLO-World
YOLOv9 Object Detection
RTMDet Object Detection
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 Pose Estimation
YOLOv8 Oriented Bounding Boxes
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
Train a YOLOv8 Classification Model with No Labeling

📸 computer vision skills (18 notebooks)

notebook	open in colab / kaggle / sagemaker studio lab	complementary materials	repository / paper
How to Estimate Vehicle Speed
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
Auto Train YOLOv8 Model with Autodistill
Image Embeddings Analysis - Part 1
Automated Dataset Annotation and Evaluation with Grounding DINO and SAM
Automated Dataset Annotation and Evaluation with Grounding DINO
Roboflow Video Inference with Custom Annotators
DINO-GPT-4V Object Detection
Train a Segmentation Model with No Labeling
DINOv2 Image Retrieval
Vector Analysis with Scikit-learn and Bokeh
RF100 Object Detection Model Benchmarking
Create Segmentation Masks with Roboflow
How to Use PolygonZone and Roboflow Supervision
Train a Package Detector With Two Labeled Images
Image-to-Image Search with CLIP and faiss

🎬 videos

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

Created: 26 May 2023 | Updated: 26 May 2023

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...

Accelerate Image Annotation with SAM and Grounding DINO

Created: 20 Apr 2023 | Updated: 20 Apr 2023

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...

SAM - Segment Anything Model by Meta AI: Complete Guide

Created: 11 Apr 2023 | Updated: 11 Apr 2023

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 git@github.com: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.

ryd-dev/Roboflow-Computer-Vision-YOLO

ErrorLooks like something went wrong!

About

Resources

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Languages

Error
Looks like something went wrong!

Packages