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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59c10f3ed5c7e7ba33a393cc3bb6c20fbbf44c02c47945c00bec7c52674f395a
- Size of remote file:
- 308 kB
- SHA256:
- b2aa9cc94eb7b0cd86062e09faaebff0138cfc7c8cde7ff248dab28faba82eb7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.