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:
- e8cd703c6e40ce1dcf906ba4d33a407b636f9ca9e491b3a2e8edfc070dc32668
- Size of remote file:
- 308 kB
- SHA256:
- d6cfe89bce1bcfffcfcdec471b660e2e0e728c2a1def04c80b79ecd30dd4a329
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.