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 .build import build_backbone, BACKBONE_REGISTRY # noqa F401 isort:skip | |
| from .backbone import Backbone | |
| from .fpn import FPN | |
| from .regnet import RegNet | |
| from .resnet import ( | |
| BasicStem, | |
| ResNet, | |
| ResNetBlockBase, | |
| build_resnet_backbone, | |
| make_stage, | |
| BottleneckBlock, | |
| ) | |
| from .vit import ViT, SimpleFeaturePyramid, get_vit_lr_decay_rate | |
| from .mvit import MViT | |
| from .swin import SwinTransformer | |
| __all__ = [k for k in globals().keys() if not k.startswith("_")] | |
| # TODO can expose more resnet blocks after careful consideration | |