Instructions to use hf-internal-testing/tiny-random-BeitBackbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BeitBackbone with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, BeitBackbone processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-BeitBackbone") model = BeitBackbone.from_pretrained("hf-internal-testing/tiny-random-BeitBackbone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-BeitBackbone: direct link, hf CLI and curl.
- Browser
- Download file 776 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-BeitBackbone/resolve/refs%2Fpr%2F110/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-BeitBackbone@refs/pr/110/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-BeitBackbone/resolve/refs%2Fpr%2F110/preprocessor_config.json
776 Bytes
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "segmentation_maps", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_center_crop", | |
| "crop_size", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "do_reduce_labels", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "crop_size": { | |
| "height": 30, | |
| "width": 30 | |
| }, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_reduce_labels": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "BeitImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 30, | |
| "width": 30 | |
| } | |
| } | |