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hkivancoral
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smids_1x_beit_base_rms_0001_fold3

Image Classification
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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_18-44-07_e7698b4f2488/events.out.tfevents.1701629048.e7698b4f2488.1907.3 +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: 0.81
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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: 1.9808
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- - Accuracy: 0.81
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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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- | 1.001 | 1.0 | 75 | 0.8924 | 0.5483 |
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- | 0.8459 | 2.0 | 150 | 0.9108 | 0.5317 |
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- | 0.8299 | 3.0 | 225 | 0.8302 | 0.5567 |
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- | 0.7786 | 4.0 | 300 | 0.8257 | 0.5817 |
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- | 0.7521 | 5.0 | 375 | 0.7371 | 0.635 |
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- | 0.6799 | 6.0 | 450 | 0.6569 | 0.6967 |
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- | 0.6268 | 7.0 | 525 | 0.6397 | 0.74 |
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- | 0.5983 | 8.0 | 600 | 0.6180 | 0.7433 |
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- | 0.5433 | 9.0 | 675 | 0.5897 | 0.7667 |
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- | 0.5488 | 10.0 | 750 | 0.5991 | 0.7617 |
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- | 0.4215 | 11.0 | 825 | 0.5611 | 0.7933 |
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- | 0.4796 | 12.0 | 900 | 0.5550 | 0.7883 |
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- | 0.3442 | 13.0 | 975 | 0.6939 | 0.74 |
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- | 0.2398 | 14.0 | 1050 | 0.6136 | 0.7883 |
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- | 0.2194 | 15.0 | 1125 | 0.6771 | 0.7883 |
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- | 0.2525 | 16.0 | 1200 | 0.6375 | 0.7983 |
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- | 0.2356 | 17.0 | 1275 | 0.7875 | 0.77 |
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- | 0.2741 | 18.0 | 1350 | 0.8214 | 0.7767 |
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- | 0.1523 | 19.0 | 1425 | 0.7154 | 0.815 |
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- | 0.1878 | 20.0 | 1500 | 0.8695 | 0.8017 |
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- | 0.1117 | 21.0 | 1575 | 0.7166 | 0.8067 |
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- | 0.0992 | 22.0 | 1650 | 0.8726 | 0.7817 |
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- | 0.1068 | 23.0 | 1725 | 0.9468 | 0.7933 |
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- | 0.0531 | 24.0 | 1800 | 1.2365 | 0.7817 |
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- | 0.101 | 25.0 | 1875 | 0.9947 | 0.8017 |
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- | 0.0964 | 26.0 | 1950 | 1.0815 | 0.7767 |
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- | 0.1448 | 27.0 | 2025 | 1.5300 | 0.7733 |
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- | 0.0819 | 28.0 | 2100 | 1.2099 | 0.7767 |
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- | 0.0459 | 29.0 | 2175 | 1.0927 | 0.8067 |
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- | 0.0558 | 30.0 | 2250 | 1.1819 | 0.8117 |
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- | 0.0901 | 31.0 | 2325 | 1.5575 | 0.7967 |
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- | 0.0332 | 32.0 | 2400 | 1.4553 | 0.8033 |
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- | 0.0698 | 33.0 | 2475 | 1.5442 | 0.7933 |
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- | 0.0604 | 34.0 | 2550 | 1.6491 | 0.8017 |
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- | 0.0781 | 35.0 | 2625 | 1.7069 | 0.7917 |
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- | 0.0129 | 36.0 | 2700 | 1.8764 | 0.7933 |
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- | 0.0265 | 37.0 | 2775 | 1.6932 | 0.8067 |
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- | 0.0272 | 38.0 | 2850 | 1.7566 | 0.7883 |
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- | 0.0432 | 39.0 | 2925 | 1.7957 | 0.8083 |
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- | 0.0088 | 40.0 | 3000 | 1.6497 | 0.8033 |
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- | 0.0008 | 41.0 | 3075 | 1.7515 | 0.7917 |
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- | 0.0332 | 42.0 | 3150 | 1.5964 | 0.81 |
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- | 0.0412 | 43.0 | 3225 | 1.6159 | 0.8017 |
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- | 0.0201 | 44.0 | 3300 | 1.6457 | 0.8033 |
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- | 0.04 | 45.0 | 3375 | 1.5707 | 0.8133 |
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- | 0.0099 | 46.0 | 3450 | 1.8199 | 0.8033 |
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- | 0.0117 | 47.0 | 3525 | 1.8917 | 0.8067 |
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- | 0.0021 | 48.0 | 3600 | 1.9758 | 0.8033 |
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- | 0.0146 | 49.0 | 3675 | 1.9833 | 0.7983 |
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- | 0.003 | 50.0 | 3750 | 1.9808 | 0.81 |
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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: 0.7133333333333334
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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
 
33
  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: 0.7846
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+ - Accuracy: 0.7133
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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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+ | 1.1199 | 1.0 | 75 | 1.1044 | 0.325 |
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+ | 1.1759 | 2.0 | 150 | 1.1239 | 0.47 |
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+ | 1.1465 | 3.0 | 225 | 0.9168 | 0.5 |
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+ | 0.8955 | 4.0 | 300 | 0.8917 | 0.5017 |
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+ | 0.8948 | 5.0 | 375 | 0.8301 | 0.5533 |
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+ | 0.9774 | 6.0 | 450 | 0.8272 | 0.5467 |
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+ | 0.8001 | 7.0 | 525 | 0.8058 | 0.5567 |
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+ | 0.7633 | 8.0 | 600 | 0.8140 | 0.545 |
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+ | 0.7814 | 9.0 | 675 | 0.7815 | 0.5733 |
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+ | 0.8175 | 10.0 | 750 | 0.7839 | 0.5633 |
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+ | 0.7605 | 11.0 | 825 | 0.7664 | 0.615 |
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+ | 0.762 | 12.0 | 900 | 0.7781 | 0.59 |
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+ | 0.6797 | 13.0 | 975 | 0.7875 | 0.575 |
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+ | 0.7699 | 14.0 | 1050 | 0.7772 | 0.6117 |
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+ | 0.6167 | 15.0 | 1125 | 0.8129 | 0.585 |
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+ | 0.7106 | 16.0 | 1200 | 0.7392 | 0.6633 |
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+ | 0.7174 | 17.0 | 1275 | 0.7176 | 0.6717 |
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+ | 0.704 | 18.0 | 1350 | 0.7772 | 0.63 |
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+ | 0.6617 | 19.0 | 1425 | 0.7359 | 0.65 |
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+ | 0.6722 | 20.0 | 1500 | 0.7009 | 0.6783 |
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+ | 0.676 | 21.0 | 1575 | 0.6946 | 0.6667 |
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+ | 0.6441 | 22.0 | 1650 | 0.7089 | 0.6917 |
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+ | 0.6565 | 23.0 | 1725 | 0.7160 | 0.665 |
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+ | 0.6009 | 24.0 | 1800 | 0.6902 | 0.6783 |
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+ | 0.6592 | 25.0 | 1875 | 0.7159 | 0.665 |
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+ | 0.6628 | 26.0 | 1950 | 0.7741 | 0.6233 |
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+ | 0.6044 | 27.0 | 2025 | 0.7147 | 0.66 |
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+ | 0.585 | 28.0 | 2100 | 0.6827 | 0.69 |
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+ | 0.5831 | 29.0 | 2175 | 0.6975 | 0.6833 |
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+ | 0.6301 | 30.0 | 2250 | 0.6815 | 0.6633 |
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+ | 0.6457 | 31.0 | 2325 | 0.6813 | 0.6817 |
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+ | 0.6492 | 32.0 | 2400 | 0.6894 | 0.6783 |
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+ | 0.5418 | 33.0 | 2475 | 0.7461 | 0.6783 |
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+ | 0.5925 | 34.0 | 2550 | 0.6773 | 0.6933 |
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+ | 0.5913 | 35.0 | 2625 | 0.6656 | 0.7083 |
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+ | 0.5761 | 36.0 | 2700 | 0.6491 | 0.7133 |
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+ | 0.528 | 37.0 | 2775 | 0.6784 | 0.7 |
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+ | 0.5718 | 38.0 | 2850 | 0.7007 | 0.6783 |
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+ | 0.5083 | 39.0 | 2925 | 0.6815 | 0.7 |
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+ | 0.5069 | 40.0 | 3000 | 0.6638 | 0.71 |
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+ | 0.4838 | 41.0 | 3075 | 0.6813 | 0.7167 |
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+ | 0.5071 | 42.0 | 3150 | 0.6709 | 0.7183 |
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+ | 0.5091 | 43.0 | 3225 | 0.6746 | 0.7167 |
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+ | 0.4355 | 44.0 | 3300 | 0.7138 | 0.71 |
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+ | 0.4287 | 45.0 | 3375 | 0.7080 | 0.7133 |
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+ | 0.3954 | 46.0 | 3450 | 0.7468 | 0.7 |
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+ | 0.3389 | 47.0 | 3525 | 0.7428 | 0.7183 |
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+ | 0.3613 | 48.0 | 3600 | 0.7469 | 0.725 |
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+ | 0.388 | 49.0 | 3675 | 0.7685 | 0.7167 |
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+ | 0.2972 | 50.0 | 3750 | 0.7846 | 0.7133 |
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  ### Framework versions
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