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. | |
| import cloudpickle | |
| class PicklableWrapper: | |
| """ | |
| Wrap an object to make it more picklable, note that it uses | |
| heavy weight serialization libraries that are slower than pickle. | |
| It's best to use it only on closures (which are usually not picklable). | |
| This is a simplified version of | |
| https://github.com/joblib/joblib/blob/master/joblib/externals/loky/cloudpickle_wrapper.py | |
| """ | |
| def __init__(self, obj): | |
| while isinstance(obj, PicklableWrapper): | |
| # Wrapping an object twice is no-op | |
| obj = obj._obj | |
| self._obj = obj | |
| def __reduce__(self): | |
| s = cloudpickle.dumps(self._obj) | |
| return cloudpickle.loads, (s,) | |
| def __call__(self, *args, **kwargs): | |
| return self._obj(*args, **kwargs) | |
| def __getattr__(self, attr): | |
| # Ensure that the wrapped object can be used seamlessly as the previous object. | |
| if attr not in ["_obj"]: | |
| return getattr(self._obj, attr) | |
| return getattr(self, attr) | |