Instructions to use tzhao3/vit-tiny-imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/vit-tiny-imagenet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tzhao3/vit-tiny-imagenet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("tzhao3/vit-tiny-imagenet") model = AutoModelForImageClassification.from_pretrained("tzhao3/vit-tiny-imagenet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from tzhao3/vit-tiny-imagenet: direct link, hf CLI and curl.
- Browser
- Download file 688 MB
-
https://huggingface.co/tzhao3/vit-tiny-imagenet/resolve/main/optimizer.pt
- Command line
-
hf download hf://tzhao3/vit-tiny-imagenet/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tzhao3/vit-tiny-imagenet/resolve/main/optimizer.pt
688 MB
- Xet hash:
- 02241bf7534c43297e6c5ba2c1df5f29cef9525aca78e525314031a57761f36f
- Size of remote file:
- 688 MB
- SHA256:
- 42c5dcd8a32f9717c170abfb9e08c0a64248d1371d90d3aaf7a4a8a2023a21f5
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