Instructions to use aryanmagoon/ViT_ImageNette with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use aryanmagoon/ViT_ImageNette with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("aryanmagoon/ViT_ImageNette", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from aryanmagoon/ViT_ImageNette: direct link, hf CLI and curl.
- Browser
- Download file 579 Bytes
-
https://huggingface.co/aryanmagoon/ViT_ImageNette/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://aryanmagoon/ViT_ImageNette/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/aryanmagoon/ViT_ImageNette/resolve/main/preprocessor_config.json
579 Bytes
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "do_normalize": true, | |
| "do_rescale": false, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |