triton7777/SeaIce

Sea Ice Segmentation — Hugging Face Space

Gradio app that runs MMSegmentation SegFormer-B5 on RGB images and returns an overlayed segmentation result.

Repo layout

• app.py — Gradio UI and inference code.
• requirements.txt — Python dependencies for the Space.
• model/
  • segformer_mit-b5_8xb1-160k_pre-cityscapes_seaicergb0-1024x1024.py — MMSeg config.
  • iter_160000.pth — trained checkpoint (tracked with Git LFS).

Run locally (optional)

pip install -r requirements.txt
python app.py

Deploy to Hugging Face Spaces

1. Create a new Space (SDK: Gradio). CPU is fine; GPU optional for faster inference.
2. Push this repo to the Space remote. Ensure LFS is enabled for the .pth:

   git lfs install
   git lfs track "model/*.pth"
   git add .gitattributes model/iter_160000.pth
   git commit -m "Add checkpoint via LFS"
   git push
   

3. The Space will build and start. First cold-start may take a few minutes while installing dependencies.

Notes

• If your config defines classes and palette, the app will use them automatically; otherwise it falls back to generated colors.
• Large input images are supported; overlay alpha can be adjusted in the UI.
• To switch checkpoints or configs, update the paths at the top of app.py.