Instructions to use abhicake/bg_removal_old with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhicake/bg_removal_old with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="abhicake/bg_removal_old", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("abhicake/bg_removal_old", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from abhicake/bg_removal_old: direct link, hf CLI and curl.
- Browser
- Download file 345 Bytes
-
https://huggingface.co/abhicake/bg_removal_old/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://abhicake/bg_removal_old/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/abhicake/bg_removal_old/resolve/main/preprocessor_config.json
345 Bytes
| { | |
| "do_normalize": true, | |
| "do_pad": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "feature_extractor_type": "ImageFeatureExtractor", | |
| "image_std": [ | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "width": 1024, | |
| "height": 1024 | |
| } | |
| } |