GitHub - zllrunning/face-parsing.PyTorch: Using modified BiSeNet for face parsing in PyTorch (original) (raw)

Skip to content

Navigation Menu

• • GitHub Copilot Write better code with AI
  • GitHub Models New Manage and compare prompts
  • GitHub Advanced Security Find and fix vulnerabilities
  • Actions Automate any workflow
  • Codespaces Instant dev environments
  • Issues Plan and track work
  • Code Review Manage code changes
  • Discussions Collaborate outside of code
  • Code Search Find more, search less
• Explore
  • Learning Pathways
  • Events & Webinars
  • Ebooks & Whitepapers
  • Customer Stories
  • Partners
  • Executive Insights
• • GitHub Sponsors Fund open source developers
  • The ReadME Project GitHub community articles
• • Enterprise platform AI-powered developer platform
• Pricing

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Sign up

Appearance settings

face-parsing.PyTorch

Contents

• Training
• Demo
• References

Training

1. Prepare training data: -- download CelebAMask-HQ dataset
   -- change file path in the prepropess_data.py and run

python prepropess_data.py

1. Train the model using CelebAMask-HQ dataset: Just run the train script:

    $ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py

If you do not wish to train the model, you can download our pre-trained model and save it in res/cp.

Demo

1. Evaluate the trained model using:

evaluate using GPU

python test.py

Face makeup using parsing maps

face-makeup.PyTorch

| | Hair | Lip | | | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Original Input | | | | Color | | |

References

• BiSeNet