Instructions to use CEBangu/vit-base-patch16-224-finetune_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CEBangu/vit-base-patch16-224-finetune_test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MEGVisionTransformer processor = AutoImageProcessor.from_pretrained("CEBangu/vit-base-patch16-224-finetune_test") model = MEGVisionTransformer.from_pretrained("CEBangu/vit-base-patch16-224-finetune_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from CEBangu/vit-base-patch16-224-finetune_test: direct link, hf CLI and curl.
- Browser
- Download file 329 Bytes
-
https://huggingface.co/CEBangu/vit-base-patch16-224-finetune_test/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://CEBangu/vit-base-patch16-224-finetune_test/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/CEBangu/vit-base-patch16-224-finetune_test/resolve/main/preprocessor_config.json
329 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTImageProcessorFast", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| } | |
| } | |