Instructions to use tzhao3/vit-CIFAR100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/vit-CIFAR100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tzhao3/vit-CIFAR100") 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-CIFAR100") model = AutoModelForImageClassification.from_pretrained("tzhao3/vit-CIFAR100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from tzhao3/vit-CIFAR100: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/tzhao3/vit-CIFAR100/resolve/main/optimizer.pt
- Command line
-
hf download hf://tzhao3/vit-CIFAR100/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tzhao3/vit-CIFAR100/resolve/main/optimizer.pt
687 MB
- Xet hash:
- 604cc8b91d1866d3fa969cef1700ff30a1dddf6e1999bcea84b815e36fa1d9c1
- Size of remote file:
- 687 MB
- SHA256:
- 6cec63a72d55fe2d19bb452a5e4501ceadd435316fec4792753ab880c8ee39cf
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