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
| # -*- coding: utf-8 -*- | |
| import warnings | |
| from .flatten import TracingAdapter | |
| from .torchscript import dump_torchscript_IR, scripting_with_instances | |
| try: | |
| from caffe2.proto import caffe2_pb2 as _tmp | |
| from caffe2.python import core | |
| # caffe2 is optional | |
| except ImportError: | |
| pass | |
| else: | |
| from .api import * | |
| # TODO: Update ONNX Opset version and run tests when a newer PyTorch is supported | |
| STABLE_ONNX_OPSET_VERSION = 11 | |
| def add_export_config(cfg): | |
| warnings.warn( | |
| "add_export_config has been deprecated and behaves as no-op function.", DeprecationWarning | |
| ) | |
| return cfg | |
| __all__ = [k for k in globals().keys() if not k.startswith("_")] | |