Instructions to use jxu124/TiO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jxu124/TiO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxu124/TiO", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jxu124/TiO", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from jxu124/TiO: direct link, hf CLI and curl.
- Browser
- Download file 340 Bytes
-
https://huggingface.co/jxu124/TiO/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://jxu124/TiO@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/jxu124/TiO/resolve/refs%2Fpr%2F2/preprocessor_config.json
340 Bytes
| { | |
| "crop_pct": 0.875, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ConvNextImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 512 | |
| } | |
| } | |