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
| # Core deep learning frameworks | |
| torch>=1.10.0 | |
| torchvision>=0.11.0 | |
| # Computer vision | |
| opencv-python>=4.5.0 | |
| Pillow>=9.0.0 | |
| # Numerical computing | |
| numpy>=1.21.0 | |
| # Hugging Face integration | |
| transformers>=4.36.0 | |
| huggingface_hub>=0.19.0 | |
| # Visualization (optional, for examples) | |
| matplotlib>=3.5.0 | |
| # Detectron2 for DensePose | |
| # Note: detectron2 is included in this repository under detectron2/ | |
| # If you need to install it separately, use: | |
| # python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' | |
| # or build from the included detectron2/ directory | |
| # Optional dependencies: | |
| # - For CUDA support, install appropriate torch version first: | |
| # pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 | |
| # - For inpainting demo (example 6): | |
| # pip install diffusers | |