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 .hflip import HFlipConverter | |
| from .to_mask import ToMaskConverter | |
| from .to_chart_result import ToChartResultConverter, ToChartResultConverterWithConfidences | |
| from .segm_to_mask import ( | |
| predictor_output_with_fine_and_coarse_segm_to_mask, | |
| predictor_output_with_coarse_segm_to_mask, | |
| resample_fine_and_coarse_segm_to_bbox, | |
| ) | |
| from .chart_output_to_chart_result import ( | |
| densepose_chart_predictor_output_to_result, | |
| densepose_chart_predictor_output_to_result_with_confidences, | |
| ) | |
| from .chart_output_hflip import densepose_chart_predictor_output_hflip | |