Image Segmentation
Transformers
PyTorch
English
garment-mask-generation
image-inpainting
fashion
garment-mask
densepose
human-parsing
Instructions to use Ekliipce/wearit-garment-mask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ekliipce/wearit-garment-mask with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Ekliipce/wearit-garment-mask")# Load model directly from transformers import GarmentMaskPipeline model = GarmentMaskPipeline.from_pretrained("Ekliipce/wearit-garment-mask", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Python | |
| __pycache__/ | |
| *.py[cod] | |
| *$py.class | |
| *.so | |
| .Python | |
| build/ | |
| develop-eggs/ | |
| dist/ | |
| downloads/ | |
| eggs/ | |
| .eggs/ | |
| lib/ | |
| lib64/ | |
| parts/ | |
| sdist/ | |
| var/ | |
| wheels/ | |
| *.egg-info/ | |
| .installed.cfg | |
| *.egg | |
| # Virtual environments | |
| venv/ | |
| env/ | |
| ENV/ | |
| # IDE | |
| .vscode/ | |
| .idea/ | |
| *.swp | |
| *.swo | |
| *~ | |
| # Jupyter | |
| .ipynb_checkpoints/ | |
| *.ipynb | |
| # Model outputs | |
| output/ | |
| batch_output/ | |
| custom_output/ | |
| visualization/ | |
| *.png | |
| *.jpg | |
| *.jpeg | |
| !example_images/*.jpg | |
| !example_images/*.png | |
| # Temporary files | |
| tmp/ | |
| densepose_/tmp/ | |
| *.tmp | |
| # OS | |
| .DS_Store | |
| Thumbs.db | |
| # Checkpoints (if not included in repo) | |
| # Uncomment these if you're not including model checkpoints | |
| # chkpt/ | |
| # *.pth | |
| # *.pkl | |
| # Large files | |
| *.zip | |
| *.tar.gz | |
| *.rar | |
| # Logs | |
| *.log | |
| logs/ | |
| # Testing | |
| .pytest_cache/ | |
| .coverage | |
| htmlcov/ | |