Instructions to use hf-internal-testing/tiny-random-ViTHybridForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTHybridForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ViTHybridForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ViTHybridForImageClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3559d6e374f2bf990a11d74d41da4d5d955717bc6cf2aa9cf71b5f96ee664697
- Size of remote file:
- 274 kB
- SHA256:
- 9c6248edcb0ac3efdae24e932a7f2e5e04be349614a1926b2e7f2cf372217163
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.