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. | |
| from .launch import * | |
| from .train_loop import * | |
| __all__ = [k for k in globals().keys() if not k.startswith("_")] | |
| # prefer to let hooks and defaults live in separate namespaces (therefore not in __all__) | |
| # but still make them available here | |
| from .hooks import * | |
| from .defaults import ( | |
| create_ddp_model, | |
| default_argument_parser, | |
| default_setup, | |
| default_writers, | |
| DefaultPredictor, | |
| DefaultTrainer, | |
| ) | |