Instructions to use ydshieh/tiny-random-ViTForMaskedImageModeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-ViTForMaskedImageModeling with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, ViTForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("ydshieh/tiny-random-ViTForMaskedImageModeling") model = ViTForMaskedImageModeling.from_pretrained("ydshieh/tiny-random-ViTForMaskedImageModeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from ydshieh/tiny-random-ViTForMaskedImageModeling: direct link, hf CLI and curl.
- Browser
- Download file 246 Bytes
-
https://huggingface.co/ydshieh/tiny-random-ViTForMaskedImageModeling/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://ydshieh/tiny-random-ViTForMaskedImageModeling/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ydshieh/tiny-random-ViTForMaskedImageModeling/resolve/main/preprocessor_config.json
246 Bytes
| { | |
| "crop_size": 30, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ViTFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "size": 30 | |
| } | |