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 .chimpnsee import register_dataset as register_chimpnsee_dataset | |
| from .coco import BASE_DATASETS as BASE_COCO_DATASETS | |
| from .coco import DATASETS as COCO_DATASETS | |
| from .coco import register_datasets as register_coco_datasets | |
| from .lvis import DATASETS as LVIS_DATASETS | |
| from .lvis import register_datasets as register_lvis_datasets | |
| DEFAULT_DATASETS_ROOT = "datasets" | |
| register_coco_datasets(COCO_DATASETS, DEFAULT_DATASETS_ROOT) | |
| register_coco_datasets(BASE_COCO_DATASETS, DEFAULT_DATASETS_ROOT) | |
| register_lvis_datasets(LVIS_DATASETS, DEFAULT_DATASETS_ROOT) | |
| register_chimpnsee_dataset(DEFAULT_DATASETS_ROOT) # pyre-ignore[19] | |