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notebooks |inference |autodistill |RF-DETR

This repository offers a growing collection of computer vision tutorials. Learn to use SOTA models like YOLOv11, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5-VL for tasks ranging from object detection, segmentation, and pose estimation to data extraction and OCR. Dive in and explore the exciting world of computer vision!

notebook	open in colab / kaggle / sagemaker studio lab	complementary materials	repository / paper
Segment Images with SAM3
Segment Videos with SAM3
Open Vocabulary Object Detection with Qwen3-VL
Fine-Tune RF-DETR Segmentation on Custom Dataset
Zero-Shot Object Detection and Segmentation with Google Gemini 2.5
Fine-Tune RF-DETR on Object Detection Dataset
Zero-Shot Object Detection and Segmentation with YOLOE
Fine-Tune YOLOv12 on Object Detection Dataset
Zero-Shot Object Detection with Qwen2.5-VL
Fine-Tune Qwen2.5-VL for JSON Data Extraction
Fine-Tune PaliGemma2 on Object Detection Dataset
Fine-Tune PaliGemma2 for JSON Data Extraction
Fine-Tune PaliGemma2 for LaTeX OCR
Fine-Tune SAM-2.1
Fine-Tune GPT-4o on Object Detection Dataset
Fine-Tune YOLO11 on Object Detection Dataset
Fine-Tune YOLO11 on Instance Segmentation Dataset
Segment Images with SAM2
Segment Videos with SAM2
Fine-Tune RT-DETR on Object Detection Dataset
Fine-Tune Florence-2 on Object Detection Dataset
Run Different Vision Tasks with Florence-2
Fine-Tune PaliGemma on Object Detection Dataset
Fine-Tune YOLOv10 on Object Detection Dataset
Zero-Shot Object Detection with YOLO-World
Fine-Tune YOLOv9 on Object Detection Dataset
Fine-Tune RTMDet on Object Detection Dataset
Segment Images with FastSAM
Fine-Tune YOLO-NAS on Object Detection Dataset
Segment Images with Segment Anything Model (SAM)
Zero-Shot Object Detection with Grounding DINO
Fine-Tune DETR Transformer on Object Detection Dataset
Classify Images with DINOv2
Fine-Tune YOLOv8 on Object Detection Dataset
Fine-Tune YOLOv8 on Pose Estimation Dataset
Fine-Tune YOLOv8 on Oriented Bounding Boxes (OBB) Dataset
Fine-Tune YOLOv8 on Instance Segmentation Dataset
Fine-Tune YOLOv8 on Classification Dataset
Fine-Tune YOLOv7 on Object Detection Dataset
Fine-Tune YOLOv7 on Instance Segmentation Dataset
Fine-Tune MT-YOLOv6 on Object Detection Dataset
Fine-Tune YOLOv5 on Object Detection Dataset
Fine-Tune YOLOv5 on Classification Dataset
Fine-Tune YOLOv5 on Instance Segmentation Dataset
Fine-Tune Faster RCNN on Instance Segmentation Dataset
Fine-Tune SegFormer on Instance Segmentation Dataset
Fine-Tune ViT on Classification Dataset
Fine-Tune Scaled-YOLOv4 on Object Detection Dataset
Fine-Tune YOLOS on Object Detection Dataset
Fine-Tune YOLOR on Object Detection Dataset
Fine-Tune YOLOX on Object Detection Dataset
Fine-Tune ResNet34 on Classification Dataset
Image Classification with OpenAI Clip
Fine-Tune YOLOv4-tiny Darknet on Object Detection Dataset
Train a YOLOv8 Classification Model with No Labeling

notebook	open in colab / kaggle / sagemaker studio lab	complementary materials	repository / paper
How to Track Objects with SORT Tracker
How to Track Objects with DeepSORT Tracker

notebook	open in colab / kaggle / sagemaker studio lab	complementary materials	repository / paper
Basketball AI: Detect NBA 3 Second Violation
Basketball AI: How to Detect Track and Identify Basketball Players
Football AI
Auto-Annotate Dataset with GroundedSAM 2
Run YOLOv7 Object Detection with OpenVINO + TorchORT
Estimate Vehicle Speed with YOLOv8
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

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 themlocally, below you will find instructions on how to do it. Remember don't install your dependencies globally, usevenv.

#clone repository and navigate to root directorygit clone git@github.com:roboflow-ai/notebooks.gitcd notebooks#setup python environment and activate itpython3 -m venv venvsource venv/bin/activate#install and run jupyter notebookpip install notebookjupyter notebook

You can now open our tutorial notebooks inAmazon SageMaker Studio Lab -a free machine learning development environment that provides the compute, storage, and security—all at no cost—foranyone 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-pushingforward libraries. If you notice that any of the notebooks is not working properly, create abug reportand let us know.

If you have an idea for a new tutorial we should do, create afeature request.We are constantly looking for new ideas. If you feel up to the task and want to create a tutorial yourself, please takea peek at ourcontribution guide. There you canfind all the information you need.

We are here for you, so don't hesitate toreach out.