# roboflow/notebooks

A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.

Repository: https://github.com/roboflow/notebooks
Canonical: https://ross.abutalabs.com/products/notebooks
Homepage: https://roboflow.com/models
Language: Jupyter Notebook
License Family: other
Topics: computer-vision, deep-learning, deep-neural-networks, image-classification, image-segmentation, object-detection, yolov5, pytorch, tutorial, yolov8, google-colab, machine-learning, zero-shot-classification, zero-shot-detection, open-vocabulary-detection, automatic-labeling-system, open-vocabulary-segmentation, paligemma, qwen, vlm
Last push: 2026-08-14T17:08:34+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 8, longevity 98
- inputs: {"age_days": 1384, "days_push": 19, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9627, forks 1490 (observed 2026-08-28T04:10:36.416212+00:00)

## What it is
A large collection of Jupyter notebook tutorials covering state-of-the-art computer vision models and techniques, from classic architectures like ResNet to modern models like YOLO11, SAM 3, RF-DETR, and vision-language models like Qwen3-VL. Notebooks are designed to run in Google Colab and cover tasks such as object detection, segmentation, pose estimation, OCR, and automatic dataset labeling.

## Use cases
- learn how to train and run YOLO object detection models
- tutorials for image segmentation with SAM
- fine-tune vision language models like PaliGemma or Qwen-VL
- automatically label datasets with zero-shot detection models
- learn computer vision fundamentals like ResNet and CNNs
- run OCR and data extraction from images
- get started with open-vocabulary detection and segmentation

## When to choose
- you want hands-on, runnable notebooks for learning state-of-the-art computer vision models
- you need step-by-step guides for fine-tuning or deploying detection, segmentation, or VLM models
- you want to prototype in Google Colab without local setup

## When to avoid
- you need a production-ready library or SDK rather than educational notebooks
- you want a maintained software package with a stable API or license
- you need non-Python or non-notebook-based learning materials

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, computer-vision, image-processing, ocr, data-science
- domain: computer-vision, deep-learning, machine-learning, tutorials, image-processing
- platform: python, cross-platform
- tags: jupyter-notebooks, object-detection, image-segmentation, yolo, vision-language-models, google-colab, zero-shot-detection, open-vocabulary, pytorch, tutorial-collection, gpu

## Member repositories
- roboflow/notebooks (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:36.416212+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:21:21.492595+00:00, confidence not recorded.
  - readme: https://github.com/roboflow/notebooks (fetched 2026-08-28T04:10:36.416212+00:00, sha 43a9d11781c5)
- Data as of 2026-08-30T08:39:29.467469+00:00.