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hkivancoral
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smids_1x_deit_small_rms_0001_fold1

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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: 0.8781302170283807
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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: 1.0108
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- - Accuracy: 0.8781
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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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- | 0.7177 | 1.0 | 76 | 0.8236 | 0.6528 |
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- | 0.4363 | 2.0 | 152 | 0.4316 | 0.8230 |
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- | 0.2766 | 3.0 | 228 | 0.6101 | 0.7980 |
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- | 0.2432 | 4.0 | 304 | 0.4381 | 0.8481 |
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- | 0.1497 | 5.0 | 380 | 0.4248 | 0.8698 |
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- | 0.0894 | 6.0 | 456 | 0.4231 | 0.8831 |
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- | 0.0974 | 7.0 | 532 | 0.6013 | 0.8564 |
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- | 0.0803 | 8.0 | 608 | 0.6193 | 0.8514 |
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- | 0.0779 | 9.0 | 684 | 0.5769 | 0.8715 |
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- | 0.0371 | 10.0 | 760 | 0.6517 | 0.8765 |
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- | 0.0538 | 11.0 | 836 | 0.6969 | 0.8664 |
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- | 0.0056 | 12.0 | 912 | 0.6926 | 0.8698 |
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- | 0.0338 | 13.0 | 988 | 0.7304 | 0.8881 |
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- | 0.0151 | 14.0 | 1064 | 0.9409 | 0.8681 |
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- | 0.0716 | 15.0 | 1140 | 0.7406 | 0.8715 |
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- | 0.023 | 16.0 | 1216 | 0.6368 | 0.8815 |
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- | 0.0001 | 17.0 | 1292 | 0.6527 | 0.8865 |
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- | 0.0352 | 18.0 | 1368 | 0.7810 | 0.8748 |
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- | 0.0463 | 19.0 | 1444 | 0.6415 | 0.8748 |
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- | 0.0239 | 20.0 | 1520 | 0.7175 | 0.8698 |
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- | 0.0023 | 21.0 | 1596 | 0.6993 | 0.8815 |
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- | 0.0106 | 22.0 | 1672 | 0.8638 | 0.8765 |
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- | 0.0168 | 23.0 | 1748 | 0.9139 | 0.8648 |
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- | 0.0022 | 24.0 | 1824 | 0.9623 | 0.8631 |
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- | 0.0096 | 25.0 | 1900 | 0.8942 | 0.8681 |
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- | 0.0094 | 26.0 | 1976 | 0.9042 | 0.8648 |
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- | 0.0039 | 27.0 | 2052 | 0.9169 | 0.8748 |
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- | 0.0027 | 28.0 | 2128 | 0.9700 | 0.8664 |
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- | 0.0053 | 29.0 | 2204 | 0.9108 | 0.8748 |
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- | 0.0 | 30.0 | 2280 | 0.9141 | 0.8765 |
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- | 0.0051 | 31.0 | 2356 | 0.9234 | 0.8798 |
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- | 0.0082 | 32.0 | 2432 | 0.9279 | 0.8748 |
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- | 0.0 | 33.0 | 2508 | 0.9373 | 0.8781 |
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- | 0.0 | 34.0 | 2584 | 0.9563 | 0.8731 |
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- | 0.0118 | 35.0 | 2660 | 0.9665 | 0.8715 |
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- | 0.0036 | 36.0 | 2736 | 0.9845 | 0.8715 |
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- | 0.0063 | 37.0 | 2812 | 0.9741 | 0.8715 |
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- | 0.0 | 38.0 | 2888 | 0.9808 | 0.8765 |
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- | 0.0029 | 39.0 | 2964 | 0.9519 | 0.8781 |
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- | 0.0 | 40.0 | 3040 | 0.9717 | 0.8781 |
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- | 0.0027 | 41.0 | 3116 | 1.0218 | 0.8715 |
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- | 0.0 | 42.0 | 3192 | 1.0179 | 0.8798 |
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- | 0.0046 | 43.0 | 3268 | 0.9932 | 0.8765 |
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- | 0.0 | 44.0 | 3344 | 1.0038 | 0.8781 |
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- | 0.0 | 45.0 | 3420 | 1.0045 | 0.8781 |
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- | 0.0 | 46.0 | 3496 | 1.0048 | 0.8781 |
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- | 0.0 | 47.0 | 3572 | 1.0071 | 0.8781 |
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- | 0.0024 | 48.0 | 3648 | 1.0092 | 0.8798 |
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- | 0.0 | 49.0 | 3724 | 1.0106 | 0.8798 |
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- | 0.0 | 50.0 | 3800 | 1.0108 | 0.8781 |
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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: 0.6811352253756261
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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: 0.7301
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+ - Accuracy: 0.6811
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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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+ | 1.1511 | 1.0 | 76 | 1.0039 | 0.4290 |
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+ | 0.9396 | 2.0 | 152 | 0.9980 | 0.4658 |
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+ | 0.9392 | 3.0 | 228 | 1.1592 | 0.3239 |
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+ | 0.9832 | 4.0 | 304 | 1.0157 | 0.4791 |
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+ | 0.9342 | 5.0 | 380 | 0.9184 | 0.4725 |
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+ | 0.951 | 6.0 | 456 | 0.9262 | 0.4958 |
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+ | 1.1061 | 7.0 | 532 | 1.1999 | 0.3406 |
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+ | 0.8983 | 8.0 | 608 | 1.5626 | 0.4207 |
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+ | 0.8399 | 9.0 | 684 | 0.8862 | 0.5242 |
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+ | 0.7906 | 10.0 | 760 | 2.9194 | 0.3255 |
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+ | 0.9054 | 11.0 | 836 | 0.8409 | 0.5476 |
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+ | 0.8842 | 12.0 | 912 | 0.8563 | 0.5409 |
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+ | 0.8173 | 13.0 | 988 | 0.9009 | 0.4958 |
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+ | 0.8653 | 14.0 | 1064 | 0.8617 | 0.5476 |
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+ | 0.7859 | 15.0 | 1140 | 0.8470 | 0.5109 |
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+ | 0.7904 | 16.0 | 1216 | 0.8290 | 0.6027 |
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+ | 0.8076 | 17.0 | 1292 | 1.0668 | 0.5326 |
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+ | 0.7582 | 18.0 | 1368 | 0.8092 | 0.5776 |
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+ | 0.8375 | 19.0 | 1444 | 0.8034 | 0.5927 |
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+ | 0.817 | 20.0 | 1520 | 0.8094 | 0.5593 |
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+ | 0.7636 | 21.0 | 1596 | 0.8786 | 0.6060 |
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+ | 0.7574 | 22.0 | 1672 | 0.7805 | 0.6093 |
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+ | 0.7196 | 23.0 | 1748 | 0.8013 | 0.6227 |
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+ | 0.746 | 24.0 | 1824 | 0.9940 | 0.5492 |
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+ | 0.698 | 25.0 | 1900 | 0.7894 | 0.6227 |
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+ | 0.7416 | 26.0 | 1976 | 0.7704 | 0.6177 |
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+ | 0.7441 | 27.0 | 2052 | 0.7868 | 0.6110 |
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+ | 0.7488 | 28.0 | 2128 | 0.7854 | 0.6294 |
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+ | 0.6844 | 29.0 | 2204 | 0.7483 | 0.6394 |
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+ | 0.7046 | 30.0 | 2280 | 0.7522 | 0.6144 |
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+ | 0.7612 | 31.0 | 2356 | 0.7237 | 0.6811 |
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+ | 0.7095 | 32.0 | 2432 | 0.7781 | 0.6060 |
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+ | 0.7219 | 33.0 | 2508 | 0.7248 | 0.6477 |
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+ | 0.7697 | 34.0 | 2584 | 0.7404 | 0.6394 |
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+ | 0.7924 | 35.0 | 2660 | 0.7779 | 0.6077 |
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+ | 0.6939 | 36.0 | 2736 | 0.7018 | 0.6628 |
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+ | 0.7175 | 37.0 | 2812 | 0.7115 | 0.6711 |
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+ | 0.663 | 38.0 | 2888 | 0.7095 | 0.6594 |
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+ | 0.7209 | 39.0 | 2964 | 0.7131 | 0.6761 |
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+ | 0.6707 | 40.0 | 3040 | 0.7148 | 0.6745 |
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+ | 0.6033 | 41.0 | 3116 | 0.7278 | 0.6761 |
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+ | 0.6657 | 42.0 | 3192 | 0.7175 | 0.6745 |
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+ | 0.5768 | 43.0 | 3268 | 0.7542 | 0.6611 |
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+ | 0.608 | 44.0 | 3344 | 0.7272 | 0.6811 |
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+ | 0.5917 | 45.0 | 3420 | 0.7194 | 0.6795 |
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+ | 0.6179 | 46.0 | 3496 | 0.7229 | 0.6828 |
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+ | 0.5513 | 47.0 | 3572 | 0.7301 | 0.6861 |
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+ | 0.5669 | 48.0 | 3648 | 0.7286 | 0.6845 |
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+ | 0.4852 | 49.0 | 3724 | 0.7286 | 0.6811 |
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+ | 0.6153 | 50.0 | 3800 | 0.7301 | 0.6811 |
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  ### Framework versions
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