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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.
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It achieves the following results on the evaluation set:
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- Loss: 1.3363
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- Accuracy: 0.4888
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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, a subset of the artbench-10 dataset. Train set size 1800, test set size 180, split equally over the 9 classes.
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It achieves the following results on the evaluation set:
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- Loss: 1.3363
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- Accuracy: 0.4888
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