Instructions to use hf-internal-testing/tiny-random-FocalNetBackbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FocalNetBackbone with Transformers:
# Load model directly from transformers import AutoImageProcessor, FocalNetBackbone processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-FocalNetBackbone") model = FocalNetBackbone.from_pretrained("hf-internal-testing/tiny-random-FocalNetBackbone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 317ff4f6f4a5e18fdb5831426d196277b0313523240a696bc352b9286c0f4504
- Size of remote file:
- 302 kB
- SHA256:
- 0002893628253a07ec573a6e0b3d57a0247aba8c92150676aebc92f905166db5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.