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
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README.md
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# vit-artworkclassifier
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. This is a subset of the artbench-10 dataset, with a train set of 1000 artworks per class and a test set of 100 artworks per class.
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It achieves the following results on the evaluation set:
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# vit-artworkclassifier
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This model returns the artwork style of any image input.
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. This is a subset of the artbench-10 dataset, with a train set of 1000 artworks per class and a test set of 100 artworks per class.
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It achieves the following results on the evaluation set:
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