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. | |
| from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator | |
| from .coco_evaluation import COCOEvaluator | |
| from .rotated_coco_evaluation import RotatedCOCOEvaluator | |
| from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset | |
| from .lvis_evaluation import LVISEvaluator | |
| from .panoptic_evaluation import COCOPanopticEvaluator | |
| from .pascal_voc_evaluation import PascalVOCDetectionEvaluator | |
| from .sem_seg_evaluation import SemSegEvaluator | |
| from .testing import print_csv_format, verify_results | |
| __all__ = [k for k in globals().keys() if not k.startswith("_")] | |