#5050th of 55 in Learning and cookbooks
notebooks
Jupyter notebooks for computer vision models, with Colab and Kaggle links
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- Stars
- 9.7k
- License
- no license
- Last commit
- Aug 2026
- Last release
- Dec 2022
- Language
- Jupyter Notebook
9,196 on 1 Apr 20269,722 on 21 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
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Overview
A collection of 61 Jupyter notebooks from Roboflow covering computer vision tasks: object detection, segmentation, tracking, OCR, keypoints and JSON data extraction. Each notebook targets a specific model such as RF-DETR, YOLO26, YOLOv12, SAM3, Qwen2.5-VL, Qwen3-VL, PaliGemma 2 or Gemini, and links to Colab, Kaggle, a paper and often a video or blog post.
Who it is for: Engineers learning to run and fine-tune computer vision models
Strengths
- Each notebook opens directly in Colab or Kaggle, so no local setup is needed
- Covers fine-tuning and zero-shot use for detection, segmentation, OCR and tracking
- Many entries link to the upstream repo, arXiv paper, blog post or YouTube walkthrough
- Recent additions include RF-DETR keypoints, YOLO26, SAM3 and Qwen3-VL
Weaknesses
- No license detected in the repository metadata
- Last tagged release is from 2022-12, so versions are not pinned by release
- Some notebooks call hosted APIs such as Gemini, which need external keys
- Centered on Roboflow's ecosystem and datasets; not a general ML course
What it needs
- GPU optional
- Needs Google Colab or Kaggle, Google Gemini API (some notebooks)
- Models: RF-DETR, YOLO26, YOLOv12, YOLOE, SAM3
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