Our take
Maintaineda few commits in the last 90 days- Activity
- 31
- Community
- 59
- Issues
- 21
- Pull requests
- 44
+23 stars in the last 16 days
This is a broad, actively maintained tutorial collection with useful hosted-notebook entry points, though it is not a single standardized training framework and its repository license is unspecified in the supplied metadata.
Good fit if
- You want practical examples spanning established and newer computer vision models and tasks.
- You prefer to start from runnable notebooks, with Colab or Kaggle links available for many tutorials.
- You value a project with a substantial contributor base and recent maintenance, without relying on one dominant contributor.
Look elsewhere if
- You need one cohesive library or application with a stable, uniform API rather than separate model-specific tutorials.
- Your adoption requires a clearly stated repository license, which is not provided in the supplied metadata.
- You need frequent formal releases or rapid issue resolution. There have been no releases in the past year, and no issues were closed in the measured recent period.
All health signals
| Last commit | 2026-08-12 (2 months ago) |
|---|---|
| Commits, last 90 days | 3 |
| Releases, last 12 months | 0 (latest 1.0.0, 2022-12-01) |
| Contributors | 41 (top contributor: 62% of commits) |
| Issues closed, last 90 days | 0 |
| Pull requests merged, last 90 days | 1 |
| Project age | 3 years |
Checked on 2026-10-23 with the GitHub API.
Overview
Roboflow's notebooks is a collection of Jupyter Notebook tutorials for learning and applying computer vision models. It covers models including YOLO, Meta's Segment Anything (SAM), RF-DETR, and Qwen vision-language models, with examples for detection, segmentation, classification, pose estimation, OCR, and data extraction.
The tutorials are aimed at practitioners who want to try a model or adapt it to a custom dataset, rather than use a single packaged application. Many notebooks link directly to Google Colab and Kaggle, making it possible to explore them in hosted notebook environments.
Key Features
- Tutorials for training object-detection models on custom datasets, including multiple YOLO versions and RF-DETR.
- Examples of image and video segmentation with SAM-family models and other segmentation approaches.
- Notebooks for classification, pose estimation, and oriented bounding box tasks.
- Zero-shot and open-vocabulary detection examples using models such as Grounding DINO, YOLO-World, and Qwen-VL.
- Vision-language model tutorials for OCR, JSON data extraction, and other image understanding tasks.
- Colab and Kaggle launch links for many tutorials, plus links to related papers, source repositories, videos, and blog posts.
Use Cases
- A computer vision learner can follow a notebook to understand a current model by running its example in Colab or Kaggle.
- An ML engineer can use a fine-tuning tutorial as a starting point for adapting a supported model to a custom dataset.
- A researcher can compare approaches to detection, segmentation, or classification by exploring tutorials for different model families.
- A developer building visual data extraction can try the OCR and vision-language model examples before choosing an approach.
What you need
Detected in the repository
- Jupyter Notebook (main language on GitHub)
- Automated checks on GitHub Actions
Getting Started
Browse the notebooks directory and open a tutorial in Jupyter, Colab, or Kaggle where available. See the README for the full tutorial index and setup details.
License in Plain Words
No standard license was detected. Without a license, the default copyright rules apply: you may read the code, but not reuse it. Check the repository or ask the authors.
Alternatives
- pytorch-deep-learning: It is a structured PyTorch course with exercises and solutions, while notebooks covers Roboflow computer-vision tutorials and hosted workflows.
| Project | Language | License | Stars | Status |
|---|---|---|---|---|
| notebooks | Jupyter Notebook | – | 9.7k | Maintained |
| pytorch-deep-learning | Jupyter Notebook | MIT | 19.1k | Maintained |
Considerations
The notebooks cover many model ecosystems, so dependencies, hardware needs, and setup steps can vary from one tutorial to another. Check the individual notebook before adopting it in a workflow, particularly for compute requirements and compatibility. The project is marked maintained and has recent commits, but recent activity is modest; it has no release in over a year, and measured issue closure has been inactive. The repository's license is not specified in the supplied metadata, so confirm licensing before redistributing or incorporating its content.
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