DEPLOYMENT.md

Hugging Face Spaces Deployment Guide

Prerequisites

1. A Hugging Face account
2. Git LFS installed locally: git lfs install
3. Model weights downloaded to the correct directories

Deployment Steps

1. Prepare Model Weights

You have two options:

Option A: Upload weights via Git LFS (Recommended for public spaces)

# Initialize Git LFS
git lfs install

# Track large files
git lfs track "*.pt"
git lfs track "*.pth"
git lfs track "*.pkl"

# Add weights
git add .gitattributes
git add detectors/*/checkpoint/pretrained/weights/best.pt
git add detectors/P2G/src/utils/classes.pkl
git commit -m "Add model weights"

Option B: Configure automatic download

1. Upload your model weights to Google Drive or another host
2. Update download_weights.py with the correct URLs
3. Weights will download automatically when the Space starts

2. Create Hugging Face Space

1. Go to https://huggingface.co/spaces
2. Click "Create new Space"
3. Choose:
   • Name: deepfake-detection-library (or your preferred name)
   • ** SDK**: Gradio
   • License: MIT
   • Hardware: CPU Basic (free) or upgrade to GPU if needed

3. Push to Hugging Face

# Add HF remote (replace YOUR_USERNAME and SPACE_NAME)
git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/SPACE_NAME

# Rename README for HF
mv README.md README_github.md
mv README_HF.md README.md

# Push to Hugging Face
git add .
git commit -m "Initial commit for HF Spaces"
git push hf main

4. Configure Space

In your Space settings on Hugging Face:

• Hardware: Start with CPU Basic (free), upgrade to GPU if needed
• Secrets: Add any API keys if needed (none required currently)
• Variables: No special environment variables needed

5. Verify Deployment

1. Wait for the Space to build (may take 5-10 minutes)
2. Test each detector with sample images
3. Check logs for any errors

File Size Considerations

• Git LFS is required for files >10MB
• Each model weight file (~100-500MB) will be stored via LFS
• Free HF Spaces have storage limits; consider:
  • Upgrading to Pro for more storage
  • Using automatic download instead of uploading weights

Troubleshooting

Space fails to build

• Check requirements.txt for incompatible versions
• Review build logs in the Space interface
• Ensure all dependencies are listed

Weights not loading

• Verify Git LFS tracked the files: git lfs ls-files
• Check file sizes: LFS pointer files are ~130 bytes
• Update download_weights.py if using automatic download

Out of memory errors

• Upgrade to GPU hardware (T4 small recommended)
• Reduce batch size or model size if possible
• Use CPU inference for deployment (already configured)

Cost Optimization

• CPU Basic (free): Works but slower
• CPU Upgrade ($0.03/hour): Faster inference
• T4 Small GPU ($0.60/hour): Needed for real-time performance

Maintenance

• Monitor Space usage in HF dashboard
• Update models by pushing new weights via Git LFS
• Check Gradio version compatibility: pip list | grep gradio

Support

For issues specific to this deployment, check:

• Gradio Documentation
• HF Spaces Documentation
• GitHub Repository