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: 1,876 Bytes
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"model_type": "garment-mask-generation",
"task": "image-segmentation",
"pipeline_tag": "image-segmentation",
"architectures": ["GarmentMaskPipeline"],
"model_config": {
"device": "cuda:0",
"output_height": 1024,
"process_size": 512,
"use_convex_hull": true,
"schp_batch_size": 12,
"save_images": false
},
"checkpoint_paths": {
"densepose_ckpt": "chkpt/DensePose",
"schp_atr_ckpt": "chkpt/SCHP/exp-schp-201908301523-atr.pth",
"schp_lip_ckpt": "chkpt/SCHP/exp-schp-201908261155-lip.pth"
},
"mask_strategies": {
"allowed_strategies": ["ellipse", "box", "poly"],
"default_probabilities": {
"ellipse": 0.50,
"box": 0.30,
"poly": 0.20
}
},
"garment_types": {
"supported_types": ["upper", "lower", "dress", "upper_body", "lower_body", "full_body", "full"],
"type_mapping": {
"upper_body": "upper",
"lower_body": "lower",
"full_body": "dress",
"full": "dress"
}
},
"image_processing": {
"default_margin_ratio": 0.1,
"default_target_ratio": [4, 3],
"default_densepose_height": 1024,
"default_densepose_width": 768,
"default_final_height": 1024,
"default_final_width": 768
},
"models_info": {
"densepose": {
"architecture": "R_50_FPN_s1x",
"framework": "detectron2",
"task": "dense_pose_estimation",
"num_body_parts": 24
},
"schp_atr": {
"architecture": "ResNet101",
"framework": "pytorch",
"task": "human_parsing",
"dataset": "ATR",
"num_classes": 18,
"input_size": [512, 512]
},
"schp_lip": {
"architecture": "ResNet101",
"framework": "pytorch",
"task": "human_parsing",
"dataset": "LIP",
"num_classes": 20,
"input_size": [473, 473]
}
},
"version": "1.0.0",
"transformers_version": "4.36.0"
}
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