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hkivancoral
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smids_1x_beit_base_rms_0001_fold4

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beit
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End of training

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  1. README.md +54 -54
  2. model.safetensors +1 -1
  3. runs/Dec03_20-14-42_e7698b4f2488/events.out.tfevents.1701634483.e7698b4f2488.1907.4 +2 -2
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: 0.7966666666666666
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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](https://huggingface.co/microsoft/beit-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: 2.5856
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- - Accuracy: 0.7967
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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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- | 1.051 | 1.0 | 75 | 0.8667 | 0.5667 |
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- | 0.7864 | 2.0 | 150 | 0.7696 | 0.5683 |
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- | 0.7692 | 3.0 | 225 | 0.7631 | 0.5767 |
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- | 0.7233 | 4.0 | 300 | 0.6504 | 0.7 |
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- | 0.661 | 5.0 | 375 | 0.6496 | 0.6867 |
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- | 0.6112 | 6.0 | 450 | 0.6176 | 0.75 |
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- | 0.6172 | 7.0 | 525 | 0.5830 | 0.755 |
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- | 0.4646 | 8.0 | 600 | 0.6075 | 0.7583 |
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- | 0.4676 | 9.0 | 675 | 0.5698 | 0.7767 |
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- | 0.4123 | 10.0 | 750 | 0.5862 | 0.7683 |
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- | 0.4184 | 11.0 | 825 | 0.7020 | 0.7767 |
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- | 0.3552 | 12.0 | 900 | 0.5999 | 0.775 |
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- | 0.3199 | 13.0 | 975 | 0.6994 | 0.76 |
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- | 0.2836 | 14.0 | 1050 | 0.6241 | 0.7983 |
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- | 0.1816 | 15.0 | 1125 | 0.8113 | 0.76 |
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- | 0.2293 | 16.0 | 1200 | 0.6993 | 0.7917 |
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- | 0.2547 | 17.0 | 1275 | 0.8410 | 0.7867 |
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- | 0.1577 | 18.0 | 1350 | 0.7837 | 0.7917 |
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- | 0.2104 | 19.0 | 1425 | 0.7966 | 0.7983 |
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- | 0.1503 | 20.0 | 1500 | 1.0030 | 0.7733 |
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- | 0.2214 | 21.0 | 1575 | 0.9608 | 0.7983 |
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- | 0.0907 | 22.0 | 1650 | 1.3067 | 0.7933 |
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- | 0.135 | 23.0 | 1725 | 1.0490 | 0.7733 |
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- | 0.0734 | 24.0 | 1800 | 1.3351 | 0.7733 |
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- | 0.1415 | 25.0 | 1875 | 0.9796 | 0.8033 |
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- | 0.0431 | 26.0 | 1950 | 1.3857 | 0.8 |
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- | 0.0644 | 27.0 | 2025 | 1.7572 | 0.7817 |
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- | 0.0913 | 28.0 | 2100 | 1.5034 | 0.785 |
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- | 0.0298 | 29.0 | 2175 | 1.3676 | 0.8117 |
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- | 0.1243 | 30.0 | 2250 | 1.2495 | 0.79 |
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- | 0.02 | 31.0 | 2325 | 1.3848 | 0.8033 |
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- | 0.051 | 32.0 | 2400 | 1.5457 | 0.7917 |
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- | 0.0539 | 33.0 | 2475 | 1.8340 | 0.765 |
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- | 0.0862 | 34.0 | 2550 | 1.5944 | 0.79 |
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- | 0.0297 | 35.0 | 2625 | 1.8673 | 0.7917 |
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- | 0.0356 | 36.0 | 2700 | 1.8200 | 0.7717 |
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- | 0.048 | 37.0 | 2775 | 1.6894 | 0.8 |
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- | 0.076 | 38.0 | 2850 | 1.7273 | 0.7933 |
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- | 0.0067 | 39.0 | 2925 | 2.3925 | 0.7817 |
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- | 0.0218 | 40.0 | 3000 | 1.8081 | 0.7917 |
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- | 0.0227 | 41.0 | 3075 | 1.8576 | 0.8 |
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- | 0.0149 | 42.0 | 3150 | 2.0201 | 0.8017 |
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- | 0.0058 | 43.0 | 3225 | 2.2685 | 0.795 |
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- | 0.0075 | 44.0 | 3300 | 2.3965 | 0.8083 |
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- | 0.0078 | 45.0 | 3375 | 2.4559 | 0.8 |
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- | 0.0556 | 46.0 | 3450 | 2.5613 | 0.8017 |
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- | 0.0163 | 47.0 | 3525 | 2.4622 | 0.7917 |
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- | 0.0052 | 48.0 | 3600 | 2.5000 | 0.7983 |
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- | 0.0067 | 49.0 | 3675 | 2.6293 | 0.8 |
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- | 0.0021 | 50.0 | 3750 | 2.5856 | 0.7967 |
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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: 0.7333333333333333
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  ---
27
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
34
  It achieves the following results on the evaluation set:
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+ - Loss: 0.6670
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+ - Accuracy: 0.7333
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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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+ | 1.1121 | 1.0 | 75 | 1.0797 | 0.495 |
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+ | 1.1167 | 2.0 | 150 | 1.0990 | 0.3383 |
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+ | 1.1124 | 3.0 | 225 | 1.0945 | 0.3583 |
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+ | 1.0914 | 4.0 | 300 | 1.0750 | 0.35 |
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+ | 1.0647 | 5.0 | 375 | 0.8667 | 0.5733 |
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+ | 0.9583 | 6.0 | 450 | 0.8905 | 0.51 |
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+ | 0.8629 | 7.0 | 525 | 0.7806 | 0.5767 |
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+ | 0.8438 | 8.0 | 600 | 0.7603 | 0.5833 |
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+ | 0.812 | 9.0 | 675 | 0.7613 | 0.595 |
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+ | 0.7427 | 10.0 | 750 | 0.8115 | 0.5917 |
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+ | 0.8147 | 11.0 | 825 | 0.7428 | 0.63 |
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+ | 0.7859 | 12.0 | 900 | 0.7365 | 0.635 |
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+ | 0.8142 | 13.0 | 975 | 0.7468 | 0.6033 |
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+ | 0.7961 | 14.0 | 1050 | 0.7567 | 0.5983 |
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+ | 0.6725 | 15.0 | 1125 | 0.7876 | 0.6067 |
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+ | 0.7608 | 16.0 | 1200 | 0.7339 | 0.635 |
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+ | 0.7146 | 17.0 | 1275 | 0.7178 | 0.645 |
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+ | 0.6646 | 18.0 | 1350 | 0.7089 | 0.67 |
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+ | 0.7767 | 19.0 | 1425 | 0.7436 | 0.6433 |
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+ | 0.7149 | 20.0 | 1500 | 0.7664 | 0.655 |
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+ | 0.7622 | 21.0 | 1575 | 0.7227 | 0.6617 |
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+ | 0.6643 | 22.0 | 1650 | 0.7547 | 0.64 |
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+ | 0.7546 | 23.0 | 1725 | 0.7439 | 0.6483 |
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+ | 0.727 | 24.0 | 1800 | 0.7101 | 0.6633 |
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+ | 0.7334 | 25.0 | 1875 | 0.7022 | 0.6583 |
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+ | 0.6824 | 26.0 | 1950 | 0.7040 | 0.6767 |
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+ | 0.7383 | 27.0 | 2025 | 0.6953 | 0.6733 |
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+ | 0.6459 | 28.0 | 2100 | 0.6860 | 0.6883 |
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+ | 0.7094 | 29.0 | 2175 | 0.6882 | 0.695 |
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+ | 0.7817 | 30.0 | 2250 | 0.6855 | 0.6883 |
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+ | 0.6417 | 31.0 | 2325 | 0.6762 | 0.705 |
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+ | 0.7236 | 32.0 | 2400 | 0.6870 | 0.6917 |
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+ | 0.6676 | 33.0 | 2475 | 0.7290 | 0.685 |
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+ | 0.5839 | 34.0 | 2550 | 0.6648 | 0.7117 |
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+ | 0.6323 | 35.0 | 2625 | 0.6543 | 0.7017 |
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+ | 0.6129 | 36.0 | 2700 | 0.6910 | 0.6883 |
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+ | 0.5785 | 37.0 | 2775 | 0.6666 | 0.7217 |
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+ | 0.6055 | 38.0 | 2850 | 0.6452 | 0.7233 |
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+ | 0.5778 | 39.0 | 2925 | 0.6586 | 0.7217 |
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+ | 0.5892 | 40.0 | 3000 | 0.6725 | 0.7233 |
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+ | 0.6346 | 41.0 | 3075 | 0.6632 | 0.715 |
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+ | 0.5806 | 42.0 | 3150 | 0.6697 | 0.7217 |
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+ | 0.6328 | 43.0 | 3225 | 0.6659 | 0.7117 |
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+ | 0.5711 | 44.0 | 3300 | 0.6651 | 0.71 |
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+ | 0.5685 | 45.0 | 3375 | 0.6727 | 0.7283 |
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+ | 0.4903 | 46.0 | 3450 | 0.6607 | 0.7383 |
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+ | 0.5197 | 47.0 | 3525 | 0.6770 | 0.7283 |
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+ | 0.5572 | 48.0 | 3600 | 0.6616 | 0.7183 |
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+ | 0.5197 | 49.0 | 3675 | 0.6636 | 0.73 |
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+ | 0.489 | 50.0 | 3750 | 0.6670 | 0.7333 |
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  ### Framework versions
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