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 .box_head import ROI_BOX_HEAD_REGISTRY, build_box_head, FastRCNNConvFCHead | |
| from .keypoint_head import ( | |
| ROI_KEYPOINT_HEAD_REGISTRY, | |
| build_keypoint_head, | |
| BaseKeypointRCNNHead, | |
| KRCNNConvDeconvUpsampleHead, | |
| ) | |
| from .mask_head import ( | |
| ROI_MASK_HEAD_REGISTRY, | |
| build_mask_head, | |
| BaseMaskRCNNHead, | |
| MaskRCNNConvUpsampleHead, | |
| ) | |
| from .roi_heads import ( | |
| ROI_HEADS_REGISTRY, | |
| ROIHeads, | |
| Res5ROIHeads, | |
| StandardROIHeads, | |
| build_roi_heads, | |
| select_foreground_proposals, | |
| ) | |
| from .cascade_rcnn import CascadeROIHeads | |
| from .rotated_fast_rcnn import RROIHeads | |
| from .fast_rcnn import FastRCNNOutputLayers | |
| from . import cascade_rcnn # isort:skip | |
| __all__ = list(globals().keys()) | |