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: 835 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # 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("_")]
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