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: 598 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # 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
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