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Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-large-patch16_fold2

Image Classification
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beit
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Eval Results
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End of training

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  1. README.md +13 -13
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6459459459459459
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6848
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- - Accuracy: 0.6459
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  ## Model description
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@@ -65,16 +65,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.2143 | 1.0 | 923 | 1.1517 | 0.6165 |
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- | 0.9069 | 2.0 | 1846 | 1.0496 | 0.6422 |
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- | 0.7165 | 3.0 | 2769 | 1.0076 | 0.6576 |
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- | 0.4527 | 4.0 | 3692 | 1.0719 | 0.6619 |
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- | 0.3647 | 5.0 | 4615 | 1.1978 | 0.6473 |
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- | 0.2513 | 6.0 | 5538 | 1.3190 | 0.6570 |
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- | 0.1369 | 7.0 | 6461 | 1.4579 | 0.65 |
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- | 0.1367 | 8.0 | 7384 | 1.5702 | 0.6543 |
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- | 0.0894 | 9.0 | 8307 | 1.6556 | 0.6486 |
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- | 0.1414 | 10.0 | 9230 | 1.6848 | 0.6459 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6483783783783784
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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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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.6891
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+ - Accuracy: 0.6484
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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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+ | 1.2166 | 1.0 | 923 | 1.1497 | 0.6165 |
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+ | 0.9097 | 2.0 | 1846 | 1.0420 | 0.6449 |
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+ | 0.7046 | 3.0 | 2769 | 1.0073 | 0.6589 |
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+ | 0.4498 | 4.0 | 3692 | 1.0708 | 0.6619 |
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+ | 0.3558 | 5.0 | 4615 | 1.1984 | 0.6481 |
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+ | 0.2479 | 6.0 | 5538 | 1.3200 | 0.6535 |
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+ | 0.136 | 7.0 | 6461 | 1.4614 | 0.6508 |
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+ | 0.1353 | 8.0 | 7384 | 1.5768 | 0.6551 |
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+ | 0.0896 | 9.0 | 8307 | 1.6599 | 0.65 |
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+ | 0.1222 | 10.0 | 9230 | 1.6891 | 0.6484 |
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  ### Framework versions
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