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: 755 Bytes
436df5c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 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
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