Instructions to use hf-internal-testing/tiny-random-Swinv2Backbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swinv2Backbone with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, Swinv2Backbone processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Swinv2Backbone") model = Swinv2Backbone.from_pretrained("hf-internal-testing/tiny-random-Swinv2Backbone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-Swinv2Backbone: direct link, hf CLI and curl.
- Browser
- Download file 342 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-Swinv2Backbone/resolve/refs%2Fpr%2F36/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Swinv2Backbone@refs/pr/36/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-Swinv2Backbone/resolve/refs%2Fpr%2F36/preprocessor_config.json
342 Bytes
| { | |
| "crop_size": 32, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "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": 32, | |
| "width": 32 | |
| } | |
| } | |