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. | |
| # pyre-unsafe | |
| import os | |
| from typing import Dict, Optional | |
| from detectron2.config import CfgNode | |
| def is_relative_local_path(path: str) -> bool: | |
| path_str = os.fsdecode(path) | |
| return ("://" not in path_str) and not os.path.isabs(path) | |
| def maybe_prepend_base_path(base_path: Optional[str], path: str): | |
| """ | |
| Prepends the provided path with a base path prefix if: | |
| 1) base path is not None; | |
| 2) path is a local path | |
| """ | |
| if base_path is None: | |
| return path | |
| if is_relative_local_path(path): | |
| return os.path.join(base_path, path) | |
| return path | |
| def get_class_to_mesh_name_mapping(cfg: CfgNode) -> Dict[int, str]: | |
| return { | |
| int(class_id): mesh_name | |
| for class_id, mesh_name in cfg.DATASETS.CLASS_TO_MESH_NAME_MAPPING.items() | |
| } | |
| def get_category_to_class_mapping(dataset_cfg: CfgNode) -> Dict[str, int]: | |
| return { | |
| category: int(class_id) | |
| for category, class_id in dataset_cfg.CATEGORY_TO_CLASS_MAPPING.items() | |
| } | |