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 . import transforms # isort:skip | |
| from .build import ( | |
| build_batch_data_loader, | |
| build_detection_test_loader, | |
| build_detection_train_loader, | |
| get_detection_dataset_dicts, | |
| load_proposals_into_dataset, | |
| print_instances_class_histogram, | |
| ) | |
| from .catalog import DatasetCatalog, MetadataCatalog, Metadata | |
| from .common import DatasetFromList, MapDataset, ToIterableDataset | |
| from .dataset_mapper import DatasetMapper | |
| # ensure the builtin datasets are registered | |
| from . import datasets, samplers # isort:skip | |
| __all__ = [k for k in globals().keys() if not k.startswith("_")] | |