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google-vit-base-patch16-224-face

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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6960989202368513
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  - name: Precision
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  type: precision
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- value: 0.6966334506335445
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  - name: Recall
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  type: recall
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- value: 0.6960989202368513
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  - name: F1
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  type: f1
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- value: 0.6957934361657124
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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
@@ -43,11 +43,11 @@ 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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3257
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- - Accuracy: 0.6961
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- - Precision: 0.6966
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- - Recall: 0.6961
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- - F1: 0.6958
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  ## Model description
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@@ -81,14 +81,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.8364 | 0.99 | 89 | 0.9453 | 0.6484 | 0.6462 | 0.6484 | 0.6385 |
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- | 0.7433 | 1.99 | 178 | 0.8876 | 0.6778 | 0.6794 | 0.6778 | 0.6730 |
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- | 0.4732 | 2.99 | 267 | 0.9043 | 0.6872 | 0.6907 | 0.6872 | 0.6841 |
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- | 0.2861 | 3.99 | 356 | 0.9865 | 0.6848 | 0.6808 | 0.6848 | 0.6813 |
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- | 0.1234 | 4.99 | 445 | 1.1048 | 0.6853 | 0.6907 | 0.6853 | 0.6872 |
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- | 0.0599 | 5.99 | 534 | 1.2362 | 0.6890 | 0.6897 | 0.6890 | 0.6876 |
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- | 0.0289 | 6.99 | 623 | 1.3141 | 0.6931 | 0.6926 | 0.6931 | 0.6921 |
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- | 0.0134 | 7.99 | 712 | 1.3257 | 0.6961 | 0.6966 | 0.6961 | 0.6958 |
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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: 0.7248574809078198
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  - name: Precision
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  type: precision
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+ value: 0.717172031675939
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  - name: Recall
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  type: recall
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+ value: 0.7248574809078198
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  - name: F1
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  type: f1
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+ value: 0.7195690317790054
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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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4531
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+ - Accuracy: 0.7249
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+ - Precision: 0.7172
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+ - Recall: 0.7249
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+ - F1: 0.7196
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.8514 | 1.0 | 290 | 0.8464 | 0.7048 | 0.7035 | 0.7048 | 0.6909 |
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+ | 0.7202 | 2.0 | 580 | 0.7791 | 0.7283 | 0.7297 | 0.7283 | 0.7111 |
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+ | 0.5455 | 3.0 | 870 | 0.7950 | 0.7285 | 0.7174 | 0.7285 | 0.7171 |
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+ | 0.334 | 4.0 | 1160 | 0.8948 | 0.7155 | 0.7152 | 0.7155 | 0.7145 |
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+ | 0.1644 | 5.0 | 1450 | 1.0820 | 0.7239 | 0.7189 | 0.7239 | 0.7194 |
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+ | 0.0482 | 6.0 | 1740 | 1.2792 | 0.7204 | 0.7144 | 0.7204 | 0.7160 |
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+ | 0.0236 | 7.0 | 2030 | 1.4162 | 0.7279 | 0.7195 | 0.7279 | 0.7209 |
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+ | 0.0049 | 8.0 | 2320 | 1.4531 | 0.7249 | 0.7172 | 0.7249 | 0.7196 |
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  ### Framework versions