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:
- 9ad983e0b5392235716c1cc340e430d258aecd688d8701aaf7244bf911a9efe4
- Size of remote file:
- 308 kB
- SHA256:
- a5c3ed8c4efe40bda3cfb28d757ff129fa59d635f9fcbb519b93b44f431abaf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.