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:
- 501c9f3c6b482f3614486f8e44df51b59cf7edf4d1b862481bd2ab121eea3c1c
- Size of remote file:
- 308 kB
- SHA256:
- 3870f30a3ae92f988cd088c31d6913c36b8be7410d73f389e441e3efd3fb3d7c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.