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: 529 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
from detectron2.data import MetadataCatalog
from detectron2.utils.file_io import PathManager
from densepose import DensePoseTransformData
def load_for_dataset(dataset_name):
path = MetadataCatalog.get(dataset_name).densepose_transform_src
densepose_transform_data_fpath = PathManager.get_local_path(path)
return DensePoseTransformData.load(densepose_transform_data_fpath)
def load_from_cfg(cfg):
return load_for_dataset(cfg.DATASETS.TEST[0])
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