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 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| from dataclasses import dataclass | |
| from typing import Optional | |
| class ShapeSpec: | |
| """ | |
| A simple structure that contains basic shape specification about a tensor. | |
| It is often used as the auxiliary inputs/outputs of models, | |
| to complement the lack of shape inference ability among pytorch modules. | |
| """ | |
| channels: Optional[int] = None | |
| height: Optional[int] = None | |
| width: Optional[int] = None | |
| stride: Optional[int] = None | |