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:
- 5844142f8b063f5b64e48e622fe3c8a03359ee4eadc45c55a836655fe50c7a34
- Size of remote file:
- 308 kB
- SHA256:
- 62afab92602a4d19212ca78c1e800d32bbd62803c985faed61179abb0d50850f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.