roboflow/notebooks resource

Health v2 · maintenance only

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9627 stars · 1490 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

learning-resource · maturity active

machine-learning computer-vision image-processing ocr data-science computer-vision deep-learning machine-learning tutorials image-processing python cross-platform jupyter-notebooks object-detection image-segmentation yolo vision-language-models google-colab zero-shot-detection open-vocabulary pytorch tutorial-collection gpu

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markdown · JSON · MCP: product_card(name="roboflow/notebooks")