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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2936593c2a4fe908aa4038815e86a4efcf00614c3c8daff37a93783866031c12
- Size of remote file:
- 344 MB
- SHA256:
- 6d79ee5b99d5a21206df139f6dcfc731cc9d97147459238647d41389b2c20f9c
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