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hkivancoral
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smids_1x_deit_small_rms_0001_fold3

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  1. README.md +54 -54
  2. model.safetensors +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: 0.8683333333333333
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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 [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1062
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- - Accuracy: 0.8683
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  ## Model description
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@@ -52,7 +52,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -65,56 +65,56 @@ 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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- | 0.9975 | 1.0 | 75 | 0.8496 | 0.5467 |
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- | 0.5061 | 2.0 | 150 | 0.5223 | 0.8067 |
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- | 0.3795 | 3.0 | 225 | 0.4405 | 0.8267 |
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- | 0.3892 | 4.0 | 300 | 0.3713 | 0.8533 |
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- | 0.1768 | 5.0 | 375 | 0.3582 | 0.88 |
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- | 0.1554 | 6.0 | 450 | 0.4330 | 0.8733 |
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- | 0.1428 | 7.0 | 525 | 0.4207 | 0.8567 |
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- | 0.0674 | 8.0 | 600 | 0.6040 | 0.8567 |
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- | 0.0307 | 9.0 | 675 | 0.7767 | 0.8317 |
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- | 0.0367 | 10.0 | 750 | 0.6480 | 0.8567 |
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- | 0.0581 | 11.0 | 825 | 0.6494 | 0.87 |
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- | 0.0608 | 12.0 | 900 | 0.5071 | 0.8667 |
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- | 0.0462 | 13.0 | 975 | 0.7332 | 0.855 |
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- | 0.0198 | 14.0 | 1050 | 0.7960 | 0.8633 |
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- | 0.0291 | 15.0 | 1125 | 0.7675 | 0.8683 |
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- | 0.0004 | 16.0 | 1200 | 0.8666 | 0.8567 |
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- | 0.0394 | 17.0 | 1275 | 0.8320 | 0.8667 |
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- | 0.0137 | 18.0 | 1350 | 0.8206 | 0.86 |
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- | 0.0055 | 19.0 | 1425 | 0.9665 | 0.8583 |
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- | 0.029 | 20.0 | 1500 | 0.8497 | 0.8683 |
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- | 0.0429 | 21.0 | 1575 | 0.9318 | 0.8717 |
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- | 0.0315 | 22.0 | 1650 | 0.9188 | 0.8567 |
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- | 0.0182 | 23.0 | 1725 | 0.8073 | 0.875 |
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- | 0.0239 | 24.0 | 1800 | 0.9607 | 0.8683 |
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- | 0.0057 | 25.0 | 1875 | 0.8991 | 0.8767 |
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- | 0.004 | 26.0 | 1950 | 0.8719 | 0.8633 |
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- | 0.0226 | 27.0 | 2025 | 0.8720 | 0.8533 |
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- | 0.0534 | 28.0 | 2100 | 0.8637 | 0.8633 |
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- | 0.0299 | 29.0 | 2175 | 0.9839 | 0.865 |
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- | 0.0001 | 30.0 | 2250 | 0.9564 | 0.8667 |
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- | 0.0001 | 31.0 | 2325 | 0.9281 | 0.8783 |
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- | 0.0024 | 32.0 | 2400 | 0.9454 | 0.875 |
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- | 0.0023 | 33.0 | 2475 | 0.9716 | 0.875 |
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- | 0.0055 | 34.0 | 2550 | 0.9822 | 0.875 |
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- | 0.009 | 35.0 | 2625 | 0.9930 | 0.865 |
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- | 0.0029 | 36.0 | 2700 | 1.0435 | 0.8717 |
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- | 0.0019 | 37.0 | 2775 | 1.0502 | 0.8683 |
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- | 0.0 | 38.0 | 2850 | 1.0112 | 0.87 |
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- | 0.0 | 39.0 | 2925 | 1.0171 | 0.8733 |
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- | 0.0 | 40.0 | 3000 | 1.0381 | 0.8733 |
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- | 0.0 | 41.0 | 3075 | 1.0120 | 0.8667 |
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- | 0.0026 | 42.0 | 3150 | 1.0208 | 0.8667 |
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- | 0.0025 | 43.0 | 3225 | 1.0419 | 0.8683 |
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- | 0.0 | 44.0 | 3300 | 1.0612 | 0.87 |
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- | 0.0025 | 45.0 | 3375 | 1.0735 | 0.8633 |
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- | 0.0025 | 46.0 | 3450 | 1.0868 | 0.8667 |
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- | 0.0049 | 47.0 | 3525 | 1.0931 | 0.87 |
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- | 0.0 | 48.0 | 3600 | 1.0992 | 0.8683 |
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- | 0.0 | 49.0 | 3675 | 1.1039 | 0.87 |
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- | 0.0045 | 50.0 | 3750 | 1.1062 | 0.8683 |
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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: 0.7016666666666667
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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 [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1553
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+ - Accuracy: 0.7017
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.14 | 1.0 | 75 | 1.1120 | 0.335 |
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+ | 1.2072 | 2.0 | 150 | 1.0986 | 0.3333 |
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+ | 0.9539 | 3.0 | 225 | 0.9334 | 0.4917 |
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+ | 0.9512 | 4.0 | 300 | 0.9203 | 0.4983 |
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+ | 0.911 | 5.0 | 375 | 1.0159 | 0.445 |
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+ | 0.9061 | 6.0 | 450 | 0.9432 | 0.5133 |
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+ | 0.8557 | 7.0 | 525 | 0.9707 | 0.5517 |
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+ | 0.796 | 8.0 | 600 | 0.8853 | 0.5633 |
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+ | 0.837 | 9.0 | 675 | 0.8169 | 0.5667 |
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+ | 0.8343 | 10.0 | 750 | 0.8015 | 0.5867 |
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+ | 0.8478 | 11.0 | 825 | 0.8424 | 0.5533 |
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+ | 0.7471 | 12.0 | 900 | 0.8480 | 0.5733 |
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+ | 0.7041 | 13.0 | 975 | 0.8701 | 0.55 |
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+ | 0.7689 | 14.0 | 1050 | 0.7602 | 0.625 |
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+ | 0.6385 | 15.0 | 1125 | 0.8263 | 0.5933 |
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+ | 0.7131 | 16.0 | 1200 | 0.7809 | 0.595 |
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+ | 0.7152 | 17.0 | 1275 | 0.8940 | 0.565 |
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+ | 0.7023 | 18.0 | 1350 | 0.7651 | 0.66 |
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+ | 0.6514 | 19.0 | 1425 | 0.7331 | 0.6783 |
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+ | 0.7116 | 20.0 | 1500 | 0.7305 | 0.6883 |
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+ | 0.6713 | 21.0 | 1575 | 0.7155 | 0.6733 |
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+ | 0.634 | 22.0 | 1650 | 0.7520 | 0.6883 |
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+ | 0.664 | 23.0 | 1725 | 0.7448 | 0.6767 |
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+ | 0.5579 | 24.0 | 1800 | 0.7383 | 0.6967 |
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+ | 0.6505 | 25.0 | 1875 | 0.7438 | 0.69 |
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+ | 0.6223 | 26.0 | 1950 | 0.7719 | 0.65 |
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+ | 0.5322 | 27.0 | 2025 | 0.7151 | 0.7017 |
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+ | 0.5674 | 28.0 | 2100 | 0.7078 | 0.6817 |
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+ | 0.493 | 29.0 | 2175 | 0.7341 | 0.71 |
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+ | 0.585 | 30.0 | 2250 | 0.7150 | 0.6867 |
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+ | 0.534 | 31.0 | 2325 | 0.7507 | 0.6967 |
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+ | 0.458 | 32.0 | 2400 | 0.7455 | 0.6983 |
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+ | 0.512 | 33.0 | 2475 | 0.6902 | 0.6967 |
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+ | 0.5074 | 34.0 | 2550 | 0.6773 | 0.6983 |
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+ | 0.512 | 35.0 | 2625 | 0.6981 | 0.7083 |
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+ | 0.452 | 36.0 | 2700 | 0.7620 | 0.7083 |
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+ | 0.4013 | 37.0 | 2775 | 0.7597 | 0.7033 |
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+ | 0.4319 | 38.0 | 2850 | 0.7472 | 0.705 |
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+ | 0.4551 | 39.0 | 2925 | 0.8012 | 0.7067 |
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+ | 0.4136 | 40.0 | 3000 | 0.7673 | 0.7133 |
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+ | 0.4092 | 41.0 | 3075 | 0.8184 | 0.7067 |
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+ | 0.412 | 42.0 | 3150 | 0.8145 | 0.7183 |
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+ | 0.4199 | 43.0 | 3225 | 0.8148 | 0.725 |
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+ | 0.3632 | 44.0 | 3300 | 0.8661 | 0.69 |
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+ | 0.2849 | 45.0 | 3375 | 0.9491 | 0.7167 |
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+ | 0.3044 | 46.0 | 3450 | 0.9227 | 0.7017 |
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+ | 0.2713 | 47.0 | 3525 | 0.9951 | 0.6983 |
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+ | 0.22 | 48.0 | 3600 | 1.0641 | 0.7017 |
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+ | 0.2276 | 49.0 | 3675 | 1.1632 | 0.6983 |
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+ | 0.2183 | 50.0 | 3750 | 1.1553 | 0.7017 |
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  ### Framework versions
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