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: 768 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 29 30 | # 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())
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