Instructions to use hf-tiny-model-private/tiny-random-ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-ViTForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ViTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8e7b83b707eb15204246ef3e22718925c4a92b35569923acfc90e03f86aeeaf
- Size of remote file:
- 195 kB
- SHA256:
- 64a0f490ab4b7b82822f0bcf9cd8e74c572fe66a3b40e9163395a734477c85ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.