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dit-base-rvlcdip-finetuned-grp-actual

Image Classification
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PyTorch
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Generated from Trainer
Eval Results
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  1. README.md +12 -12
  2. pytorch_model.bin +1 -1
README.md CHANGED
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  ---
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- base_model: microsoft/dit-base-finetuned-rvlcdip
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  tags:
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  - generated_from_trainer
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  datasets:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7575757575757576
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,10 +29,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # dit-base-rvlcdip-finetuned-grp-actual
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- This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3033
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- - Accuracy: 0.7576
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.3577 | 0.96 | 18 | 2.0863 | 0.5114 |
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- | 2.0601 | 1.97 | 37 | 1.8154 | 0.6477 |
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- | 1.8068 | 2.99 | 56 | 1.5881 | 0.6705 |
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- | 1.5953 | 4.0 | 75 | 1.4112 | 0.7159 |
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- | 1.4304 | 4.96 | 93 | 1.3033 | 0.7576 |
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- | 1.3458 | 5.97 | 112 | 1.2401 | 0.75 |
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- | 1.3523 | 6.72 | 126 | 1.2240 | 0.7576 |
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  ### Framework versions
 
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  ---
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+ base_model: Tidzo/dit-base-rvlcdip-finetuned-grp-actual
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8977272727272727
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  # dit-base-rvlcdip-finetuned-grp-actual
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+ This model is a fine-tuned version of [Tidzo/dit-base-rvlcdip-finetuned-grp-actual](https://huggingface.co/Tidzo/dit-base-rvlcdip-finetuned-grp-actual) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4582
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+ - Accuracy: 0.8977
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.8692 | 0.96 | 18 | 0.6972 | 0.8561 |
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+ | 0.7348 | 1.97 | 37 | 0.6350 | 0.8598 |
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+ | 0.6655 | 2.99 | 56 | 0.5339 | 0.8712 |
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+ | 0.7167 | 4.0 | 75 | 0.5046 | 0.8902 |
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+ | 0.694 | 4.96 | 93 | 0.5026 | 0.8864 |
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+ | 0.6638 | 5.97 | 112 | 0.4601 | 0.9015 |
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+ | 0.6618 | 6.72 | 126 | 0.4582 | 0.8977 |
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  ### Framework versions
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