Image Segmentation
ONNX
BiRefNet
Chinese
background-removal
matting
rembg
sam
u2net
clipseg
vitmatte
Instructions to use jkyy/yang-pixelcut-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use jkyy/yang-pixelcut-models with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("jkyy/yang-pixelcut-models", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("jkyy/yang-pixelcut-models") - Notebooks
- Google Colab
- Kaggle
Download models/clipseg/preprocessor_config.json from jkyy/yang-pixelcut-models: direct link, hf CLI and curl.
- Browser
- Download file 378 Bytes
-
https://huggingface.co/jkyy/yang-pixelcut-models/resolve/main/models/clipseg/preprocessor_config.json
- Command line
-
hf download hf://jkyy/yang-pixelcut-models/models/clipseg/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/jkyy/yang-pixelcut-models/resolve/main/models/clipseg/preprocessor_config.json
378 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "processor_class": "CLIPSegProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 352, | |
| "width": 352 | |
| } | |
| } | |