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hushem_5x_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: 0.8372093023255814
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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: 1.8513
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- - Accuracy: 0.8372
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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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- | 1.4647 | 1.0 | 28 | 1.8602 | 0.2558 |
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- | 1.3185 | 2.0 | 56 | 1.0840 | 0.4884 |
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- | 0.9898 | 3.0 | 84 | 1.3302 | 0.3721 |
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- | 0.9442 | 4.0 | 112 | 1.0743 | 0.5349 |
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- | 0.6714 | 5.0 | 140 | 1.1638 | 0.5814 |
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- | 0.5907 | 6.0 | 168 | 0.8481 | 0.7442 |
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- | 0.3843 | 7.0 | 196 | 0.5582 | 0.7907 |
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- | 0.2836 | 8.0 | 224 | 0.9826 | 0.6977 |
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- | 0.163 | 9.0 | 252 | 0.9953 | 0.7907 |
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- | 0.0747 | 10.0 | 280 | 0.9182 | 0.8140 |
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- | 0.0702 | 11.0 | 308 | 0.8756 | 0.7907 |
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- | 0.0697 | 12.0 | 336 | 1.2367 | 0.7907 |
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- | 0.0531 | 13.0 | 364 | 1.5496 | 0.7442 |
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- | 0.0055 | 14.0 | 392 | 1.2182 | 0.8140 |
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- | 0.0148 | 15.0 | 420 | 1.4816 | 0.8140 |
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- | 0.0259 | 16.0 | 448 | 1.3748 | 0.7907 |
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- | 0.0208 | 17.0 | 476 | 1.5049 | 0.7209 |
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- | 0.0278 | 18.0 | 504 | 1.1689 | 0.8140 |
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- | 0.0002 | 19.0 | 532 | 1.6137 | 0.8372 |
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- | 0.0001 | 20.0 | 560 | 1.6368 | 0.8372 |
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- | 0.0 | 21.0 | 588 | 1.6426 | 0.8372 |
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- | 0.0 | 22.0 | 616 | 1.6498 | 0.8372 |
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- | 0.0 | 23.0 | 644 | 1.6573 | 0.8372 |
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- | 0.0 | 24.0 | 672 | 1.6654 | 0.8372 |
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- | 0.0 | 25.0 | 700 | 1.6746 | 0.8372 |
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- | 0.0 | 26.0 | 728 | 1.6832 | 0.8372 |
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- | 0.0 | 27.0 | 756 | 1.6985 | 0.8372 |
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- | 0.0 | 28.0 | 784 | 1.7057 | 0.8372 |
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- | 0.0 | 29.0 | 812 | 1.7143 | 0.8372 |
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- | 0.0 | 30.0 | 840 | 1.7226 | 0.8372 |
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- | 0.0 | 31.0 | 868 | 1.7340 | 0.8372 |
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- | 0.0 | 32.0 | 896 | 1.7422 | 0.8372 |
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- | 0.0 | 33.0 | 924 | 1.7506 | 0.8372 |
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- | 0.0 | 34.0 | 952 | 1.7590 | 0.8372 |
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- | 0.0 | 35.0 | 980 | 1.7673 | 0.8372 |
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- | 0.0 | 36.0 | 1008 | 1.7761 | 0.8372 |
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- | 0.0 | 37.0 | 1036 | 1.7852 | 0.8372 |
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- | 0.0 | 38.0 | 1064 | 1.7939 | 0.8372 |
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- | 0.0 | 39.0 | 1092 | 1.8014 | 0.8372 |
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- | 0.0 | 40.0 | 1120 | 1.8097 | 0.8372 |
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- | 0.0 | 41.0 | 1148 | 1.8172 | 0.8372 |
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- | 0.0 | 42.0 | 1176 | 1.8240 | 0.8372 |
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- | 0.0 | 43.0 | 1204 | 1.8302 | 0.8372 |
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- | 0.0 | 44.0 | 1232 | 1.8365 | 0.8372 |
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- | 0.0 | 45.0 | 1260 | 1.8414 | 0.8372 |
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- | 0.0 | 46.0 | 1288 | 1.8453 | 0.8372 |
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- | 0.0 | 47.0 | 1316 | 1.8499 | 0.8372 |
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- | 0.0 | 48.0 | 1344 | 1.8512 | 0.8372 |
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- | 0.0 | 49.0 | 1372 | 1.8513 | 0.8372 |
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- | 0.0 | 50.0 | 1400 | 1.8513 | 0.8372 |
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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: 0.9302325581395349
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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: 0.6867
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+ - Accuracy: 0.9302
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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: 1e-05
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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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+ | 0.8625 | 1.0 | 28 | 0.7655 | 0.7442 |
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+ | 0.213 | 2.0 | 56 | 0.3346 | 0.9070 |
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+ | 0.0427 | 3.0 | 84 | 0.3169 | 0.8837 |
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+ | 0.0088 | 4.0 | 112 | 0.2583 | 0.9070 |
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+ | 0.0019 | 5.0 | 140 | 0.3591 | 0.9070 |
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+ | 0.0013 | 6.0 | 168 | 0.3828 | 0.8837 |
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+ | 0.0008 | 7.0 | 196 | 0.3548 | 0.9070 |
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+ | 0.0007 | 8.0 | 224 | 0.3758 | 0.9302 |
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+ | 0.0005 | 9.0 | 252 | 0.4221 | 0.9070 |
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+ | 0.0004 | 10.0 | 280 | 0.4042 | 0.9302 |
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+ | 0.0004 | 11.0 | 308 | 0.4067 | 0.9302 |
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+ | 0.0003 | 12.0 | 336 | 0.4099 | 0.9302 |
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+ | 0.0002 | 13.0 | 364 | 0.4211 | 0.9302 |
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+ | 0.0002 | 14.0 | 392 | 0.4323 | 0.9302 |
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+ | 0.0002 | 15.0 | 420 | 0.4463 | 0.9302 |
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+ | 0.0002 | 16.0 | 448 | 0.4550 | 0.9302 |
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+ | 0.0001 | 17.0 | 476 | 0.4114 | 0.9302 |
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+ | 0.0001 | 18.0 | 504 | 0.4747 | 0.9302 |
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+ | 0.0001 | 19.0 | 532 | 0.4880 | 0.9302 |
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+ | 0.0001 | 20.0 | 560 | 0.4798 | 0.9302 |
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+ | 0.0001 | 21.0 | 588 | 0.5078 | 0.9302 |
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+ | 0.0001 | 22.0 | 616 | 0.5161 | 0.9302 |
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+ | 0.0001 | 23.0 | 644 | 0.5322 | 0.9302 |
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+ | 0.0001 | 24.0 | 672 | 0.5391 | 0.9302 |
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+ | 0.0 | 25.0 | 700 | 0.5387 | 0.9302 |
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+ | 0.0 | 26.0 | 728 | 0.5518 | 0.9302 |
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+ | 0.0 | 27.0 | 756 | 0.5912 | 0.9302 |
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+ | 0.0 | 28.0 | 784 | 0.5723 | 0.9302 |
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+ | 0.0 | 29.0 | 812 | 0.5780 | 0.9302 |
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+ | 0.0 | 30.0 | 840 | 0.5909 | 0.9302 |
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+ | 0.0 | 31.0 | 868 | 0.5963 | 0.9302 |
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+ | 0.0 | 32.0 | 896 | 0.6068 | 0.9302 |
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+ | 0.0 | 33.0 | 924 | 0.6067 | 0.9302 |
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+ | 0.0 | 34.0 | 952 | 0.6107 | 0.9302 |
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+ | 0.0 | 35.0 | 980 | 0.5868 | 0.9302 |
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+ | 0.0 | 36.0 | 1008 | 0.6292 | 0.9302 |
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+ | 0.0 | 37.0 | 1036 | 0.6428 | 0.9302 |
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+ | 0.0 | 38.0 | 1064 | 0.6729 | 0.9070 |
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+ | 0.0 | 39.0 | 1092 | 0.6601 | 0.9302 |
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+ | 0.0 | 40.0 | 1120 | 0.6776 | 0.9070 |
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+ | 0.0 | 41.0 | 1148 | 0.6683 | 0.9302 |
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+ | 0.0 | 42.0 | 1176 | 0.6712 | 0.9302 |
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+ | 0.0 | 43.0 | 1204 | 0.6721 | 0.9302 |
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+ | 0.0 | 44.0 | 1232 | 0.6819 | 0.9302 |
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+ | 0.0 | 45.0 | 1260 | 0.6829 | 0.9302 |
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+ | 0.0 | 46.0 | 1288 | 0.6827 | 0.9302 |
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+ | 0.0 | 47.0 | 1316 | 0.6864 | 0.9302 |
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+ | 0.0 | 48.0 | 1344 | 0.6865 | 0.9302 |
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+ | 0.0 | 49.0 | 1372 | 0.6867 | 0.9302 |
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+ | 0.0 | 50.0 | 1400 | 0.6867 | 0.9302 |
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  ### Framework versions
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