roboflow/notebooks
roboflow/notebooks has 9,744 GitHub stars and 1,502 forks. GitHot measured +1 stars in 24 hours, +9 in 7 days, and +21 in 15 days. Last checked 2026-11-07 19:13 UTC. Last push 2026-09-22.
최첨단 컴퓨터 비전 모델과 기법에 관한 튜토리얼 모음입니다. ResNet과 같은 기초 아키텍처부터 RF-DETR, YOLO11, SAM 3, Qwen3-VL과 같은 첨단 모델까지 폭넓게 살펴볼 수 있습니다.
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README.md
Original👋 hello
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!
🚀 model tutorials (61 notebooks)
📍 tracker tutorials (4 notebooks)
| notebook | open in colab / kaggle / sagemaker studio lab | complementary materials | repository / paper |
|---|---|---|---|
| How to Track Objects with RF-DETR and ByteTrack Tracker | |||
| How to Track Objects with RF-DETR and SORT Tracker | |||
| How to Track Objects with RF-DETR and OC-SORT Tracker | |||
| How to Track Objects with RF-DETR and McByte Tracker |
🛠️ computer vision skills (23 notebooks)
🎬 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
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
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
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 [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
☁️ 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.
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