DEPLOY_HF_SPACES.md

Deploy to Hugging Face Spaces (Gradio)

This project is now ready for Hugging Face Spaces.

Option A (recommended): single Space repo with checkpoints

Use this when you want the simplest deployment.

1. Create a new Hugging Face Space:

• SDK: Gradio
• Hardware: CPU Basic to start, upgrade to GPU for faster inference

2. Push this project to that Space repo.

3. Ensure these files are present at the Space repo root:

• app.py
• requirements.txt
• configs/config.yaml
• models/
• data/
• utils/
• checkpoints/changeformer_best.pth (or your preferred model)

4. In Space Settings, set startup file to app.py (default for Gradio Spaces).

5. Optional: reduce initial footprint by keeping only one checkpoint (for example changeformer_best.pth) inside checkpoints/.

Option B: Space app + separate model repo

Use this when you want a smaller Space repo and keep large checkpoints elsewhere.

1. Upload checkpoint files to a separate Hugging Face model repo.

2. In your Space Settings -> Variables, set:

• HF_MODEL_REPO: owner/repo-name
• HF_MODEL_REVISION: optional branch/tag/commit (for reproducible deployment)

3. On startup, app.py will auto-download expected checkpoint filenames into checkpoints/.

Expected checkpoint names:

• siamese_cnn_best.pth
• unet_pp_best.pth
• changeformer_best.pth

Space README metadata (required in Space repo)

In the Space repository README.md, include this at the top:

---
title: Military Base Change Detection
emoji: satellite
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
python_version: 3.10
---

Notes

• CPU hardware works, but inference can be slow for larger images.
• For better latency, choose a GPU Space.
• app.py now detects Spaces automatically and binds to 0.0.0.0.
• If no local checkpoints are found, it will try HF_MODEL_REPO.

Quick local validation before push

pip install -r requirements.txt
python app.py

Then open the local Gradio URL and test one sample pair.