Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use oschamp/vit-artworkclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oschamp/vit-artworkclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="oschamp/vit-artworkclassifier") 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("oschamp/vit-artworkclassifier") model = AutoModelForImageClassification.from_pretrained("oschamp/vit-artworkclassifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b646625b905622d70504566d883f40d9862b12e013aaafa0a866a3c04b64c14
- Size of remote file:
- 343 MB
- SHA256:
- fa4b52595c9f1ae8aabee148abbf8e0e816620c266821f687930ff4b31bded53
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