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 .boxes import Boxes, BoxMode, pairwise_iou, pairwise_ioa, pairwise_point_box_distance | |
| from .image_list import ImageList | |
| from .instances import Instances | |
| from .keypoints import Keypoints, heatmaps_to_keypoints | |
| from .masks import BitMasks, PolygonMasks, polygons_to_bitmask, ROIMasks | |
| from .rotated_boxes import RotatedBoxes | |
| from .rotated_boxes import pairwise_iou as pairwise_iou_rotated | |
| __all__ = [k for k in globals().keys() if not k.startswith("_")] | |
| from detectron2.utils.env import fixup_module_metadata | |
| fixup_module_metadata(__name__, globals(), __all__) | |
| del fixup_module_metadata | |