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:
# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d41957972d512c562cc415bb2c1a3437e1ad95d17695c6140c8ba36ac5bd303
- Size of remote file:
- 308 kB
- SHA256:
- 8a007e13201a1d788edaa207b1fc539f76c22ead34549f4cf7305a2b716b628c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.