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 | |
| from .data.datasets import builtin # just to register data | |
| from .converters import builtin as builtin_converters # register converters | |
| from .config import ( | |
| add_densepose_config, | |
| add_densepose_head_config, | |
| add_hrnet_config, | |
| add_dataset_category_config, | |
| add_bootstrap_config, | |
| load_bootstrap_config, | |
| ) | |
| from .structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData | |
| from .evaluation import DensePoseCOCOEvaluator | |
| from .modeling.roi_heads import DensePoseROIHeads | |
| from .modeling.test_time_augmentation import ( | |
| DensePoseGeneralizedRCNNWithTTA, | |
| DensePoseDatasetMapperTTA, | |
| ) | |
| from .utils.transform import load_from_cfg | |
| from .modeling.hrfpn import build_hrfpn_backbone | |