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vit-base-patch16-224-in21k_covid_19_ct_scans

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  1. README.md +22 -22
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9765258215962441
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  - name: F1
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  type: f1
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- value: 0.9294374875770225
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  - name: Recall
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  type: recall
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- value: 1.0
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  - name: Precision
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  type: precision
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- value: 0.9744897959183674
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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
@@ -44,12 +44,12 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.1064
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- - Accuracy: 0.9765
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- - F1: 0.9294
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- - Auc: 0.8864
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- - Recall: 1.0
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- - Precision: 0.9745
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  ## Model description
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@@ -80,18 +80,18 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Recall | Precision |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
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- | 0.6804 | 1.0 | 54 | 0.3821 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 |
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- | 0.6804 | 2.0 | 108 | 0.4134 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 |
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- | 0.6804 | 3.0 | 162 | 0.2708 | 0.9061 | 0.5585 | 0.5455 | 1.0 | 0.9052 |
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- | 0.6804 | 4.0 | 216 | 0.2405 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 |
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- | 0.6804 | 5.0 | 270 | 0.2193 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 |
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- | 0.6804 | 6.0 | 324 | 0.1719 | 0.9484 | 0.8775 | 0.9310 | 0.9529 | 0.9891 |
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- | 0.6804 | 7.0 | 378 | 0.0525 | 0.9859 | 0.9612 | 0.9519 | 0.9948 | 0.9896 |
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- | 0.6804 | 8.0 | 432 | 0.0482 | 0.9906 | 0.9736 | 0.9545 | 1.0 | 0.9896 |
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- | 0.6804 | 9.0 | 486 | 0.0907 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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- | 0.1258 | 10.0 | 540 | 0.1009 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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- | 0.1258 | 11.0 | 594 | 0.1051 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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- | 0.1258 | 12.0 | 648 | 0.1064 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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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: 0.9197530864197531
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  - name: F1
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  type: f1
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+ value: 0.6365832614322692
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  - name: Recall
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  type: recall
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+ value: 0.9931972789115646
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  - name: Precision
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  type: precision
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+ value: 0.9240506329113924
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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 [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: 0.5002
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+ - Accuracy: 0.9198
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+ - F1: 0.6366
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+ - Auc: 0.5966
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+ - Recall: 0.9932
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+ - Precision: 0.9241
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Recall | Precision |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
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+ | 0.7767 | 1.0 | 47 | 0.3346 | 0.9074 | 0.4757 | 0.5 | 1.0 | 0.9074 |
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+ | 0.7767 | 2.0 | 94 | 0.5513 | 0.8272 | 0.5919 | 0.6204 | 0.8741 | 0.9312 |
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+ | 0.7767 | 3.0 | 141 | 0.4290 | 0.9074 | 0.4757 | 0.5 | 1.0 | 0.9074 |
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+ | 0.7767 | 4.0 | 188 | 0.4333 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
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+ | 0.7767 | 5.0 | 235 | 0.5041 | 0.9074 | 0.6181 | 0.5898 | 0.9796 | 0.9231 |
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+ | 0.7767 | 6.0 | 282 | 0.4848 | 0.9167 | 0.6317 | 0.5949 | 0.9898 | 0.9238 |
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+ | 0.7767 | 7.0 | 329 | 0.4877 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
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+ | 0.7767 | 8.0 | 376 | 0.4926 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
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+ | 0.7767 | 9.0 | 423 | 0.4958 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
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+ | 0.7767 | 10.0 | 470 | 0.4981 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
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+ | 0.0381 | 11.0 | 517 | 0.4996 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
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+ | 0.0381 | 12.0 | 564 | 0.5002 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 |
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  ### Framework versions
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