Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations using user-defined labels
| Date | Stars |
|---|---|
| 2026-07-24 | 594 |
| 2026-07-25 | 594 |
| 2026-07-28 | 594 |
| 2026-07-30 | 594 |
| 2026-08-06 | 594 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Anno-Mage: A Semi Automatic Image Annotation Tool [](https://pypi.org/project/anno-mage/) [](https://pypi.org/project/anno-mage/) [](https://github.com/virajmavani/semi-auto-image-annotation-tool/actions/workflows/release.yml) [](https://github.com/virajmavani/semi-auto-image-annotation-tool/actions/workflows/tests.yml) [](LICENSE)  Semi-automatic image annotation toolbox powered by PyTorch object detection models, including open-vocabulary zero-shot detection via OWL-v2. Available as a **web app** (FastAPI + React). --- ## Web App See [`web/README.md`](web/README.md) for installation, usage, and API reference. **Quick start:** ```bash # Backend (port 8000) cd web/backend && python main.py # Frontend (port 3000) cd web/frontend && npm install && npm run dev ``` Or use the convenience script: ```bash cd web && bash start.sh ``` --- ## PyPI Distribution ### Install from PyPI ```bash pip install anno-mage anno-mage ``` The app opens in your browser automatically. Annotations are saved to `~/.anno-mage/annotations/`. ### Publish a Release Releases publish automatically to PyPI when a version tag is pushed. GitHub Actions builds the frontend, packages everything, and publishes via PyPI Trusted Publishers (no tokens required). **One-time PyPI setup:** 1. Go to your PyPI project → *Manage* → *Publishing* → *Add a new publisher* 2. Set: GitHub repo `virajmavani/semi-auto-image-annotation-tool`, workflow `release.yml`, environment `pypi` **To release:** ```bash git tag v2.0.1 git push origin v2.0.1 ``` That's it — the workflow in `.github/workflows/release.yml` handles the rest. ### Build Locally To build the package without publishing: **Prerequisites:** ```bash pip install build npm install # inside web/frontend if not already done ``` ```bash bash build_release.sh ``` This compiles the React frontend, copies the build into `anno_mage/static/`, and produces wheel and sdist artifacts in `dist/`. --- ## Output Formats Both interfaces produce identical output: | Format | Location | Description | |--------|----------|-------------| | CSV | `annotations/annotations.csv` | `image_path,x1,y1,x2,y2,label` per row | | Pascal VOC XML | `annotations/annotations_voc/` | One XML file per image | --- ## Acknowledgments - [Meditab Software Inc.](https://www.meditab.com/) - [PyTorch / Torchvision](https://pytorch.org/) for the RetinaNet implementation - [HuggingFace Transformers](https://huggingface.co/google/owlv2-base-patch16-ensemble) for the OWL-v2 zero-shot detection model - [Computer Vision Group](https://cvgldce.github.io/), L.D. College of Engineering ### Join the developers channel Slack: https://join.slack.com/t/annomage/shared_invite/zt-dh4ca9du-4VOcwUMCSNA6lmyG~tNUPg
Excerpt of 3,298 characters
Read on GitHub
73
9
2
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cc81c01c94084ff7, topic:deep-learning, topic:pytorch, topic:tensorflow