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
| { | |
| "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" | |
| } | |