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 .meshes import builtin | |
| from .build import ( | |
| build_detection_test_loader, | |
| build_detection_train_loader, | |
| build_combined_loader, | |
| build_frame_selector, | |
| build_inference_based_loaders, | |
| has_inference_based_loaders, | |
| BootstrapDatasetFactoryCatalog, | |
| ) | |
| from .combined_loader import CombinedDataLoader | |
| from .dataset_mapper import DatasetMapper | |
| from .inference_based_loader import InferenceBasedLoader, ScoreBasedFilter | |
| from .image_list_dataset import ImageListDataset | |
| from .utils import is_relative_local_path, maybe_prepend_base_path | |
| # ensure the builtin datasets are registered | |
| from . import datasets | |
| # ensure the bootstrap datasets builders are registered | |
| from . import build | |
| __all__ = [k for k in globals().keys() if not k.startswith("_")] | |