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
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # pyre-unsafe | |
| from .confidence import DensePoseConfidenceModelConfig, DensePoseUVConfidenceType | |
| from .filter import DensePoseDataFilter | |
| from .inference import densepose_inference | |
| from .utils import initialize_module_params | |
| from .build import ( | |
| build_densepose_data_filter, | |
| build_densepose_embedder, | |
| build_densepose_head, | |
| build_densepose_losses, | |
| build_densepose_predictor, | |
| ) | |