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  metrics:
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  - f1
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  model-index:
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- - name: vit_tickers_binaryclf
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  results: []
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  ---
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- # vit_tickers_binaryclf
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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 cord, rvl-cdip, visual-genome and an external receipt tickers dataset, to carry out Binary Classification (`ticket` vs `no_ticket`).
 
 
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  It achieves the following results on the evaluation set, which contain pictures from the above datasets in scanned, photography or mobile picture formats (color and grayscale):
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  - Loss: 0.0116
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  ## Model description
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- This model is a Binary Classifier finetuned version of ViT, to predict if an input image is a picture / scan of ticket(s) o something else.
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  ## Intended uses & limitations
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  metrics:
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  - f1
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  model-index:
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+ - name: vit_receipts_classifier
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  results: []
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  ---
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+ # vit_receipts_classifier
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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 cord, rvl-cdip, visual-genome and an external receipt dataset to carry out Binary Classification (`ticket` vs `no_ticket`).
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+
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+ Ticket here is used as a synonym to "receipt".
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  It achieves the following results on the evaluation set, which contain pictures from the above datasets in scanned, photography or mobile picture formats (color and grayscale):
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  - Loss: 0.0116
 
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  ## Model description
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+ This model is a Binary Classifier finetuned version of ViT, to predict if an input image is a picture / scan of receipts(s) o something else.
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  ## Intended uses & limitations
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