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 .chart import DensePoseChartPredictorOutput | |
| from .chart_confidence import decorate_predictor_output_class_with_confidences | |
| from .cse_confidence import decorate_cse_predictor_output_class_with_confidences | |
| from .chart_result import ( | |
| DensePoseChartResult, | |
| DensePoseChartResultWithConfidences, | |
| quantize_densepose_chart_result, | |
| compress_quantized_densepose_chart_result, | |
| decompress_compressed_densepose_chart_result, | |
| ) | |
| from .cse import DensePoseEmbeddingPredictorOutput | |
| from .data_relative import DensePoseDataRelative | |
| from .list import DensePoseList | |
| from .mesh import Mesh, create_mesh | |
| from .transform_data import DensePoseTransformData, normalized_coords_transform | |