Instructions to use tmshag1/vitmodel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmshag1/vitmodel2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tmshag1/vitmodel2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("tmshag1/vitmodel2", trust_remote_code=True) model = AutoModel.from_pretrained("tmshag1/vitmodel2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from tmshag1/vitmodel2: direct link, hf CLI and curl.
- Browser
- Download file 160 Bytes
-
https://huggingface.co/tmshag1/vitmodel2/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://tmshag1/vitmodel2@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/tmshag1/vitmodel2/resolve/refs%2Fpr%2F1/preprocessor_config.json
160 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "size": 224 | |
| } | |