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")# 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
- Xet hash:
- acca43172845b4051cc3e6387c642b58c58a6f05a9e8e2c6cf89a6b1a0117e6a
- Size of remote file:
- 3.9 kB
- SHA256:
- d5c14c69a507899df9fb75ddc65552a9d9ec21e94493591278fb81115682933e
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