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
File size: 698 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # 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]
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