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hushem_5x_deit_small_rms_0001_fold2

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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: 0.8
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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: 1.9113
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- - Accuracy: 0.8
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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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- | 1.4554 | 1.0 | 27 | 1.3903 | 0.2444 |
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- | 1.4027 | 2.0 | 54 | 1.3027 | 0.4444 |
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- | 1.2315 | 3.0 | 81 | 1.0694 | 0.5556 |
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- | 1.0842 | 4.0 | 108 | 1.2537 | 0.4889 |
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- | 0.7655 | 5.0 | 135 | 1.0709 | 0.6 |
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- | 0.5266 | 6.0 | 162 | 0.8849 | 0.7111 |
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- | 0.3165 | 7.0 | 189 | 0.8247 | 0.7778 |
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- | 0.1539 | 8.0 | 216 | 1.3972 | 0.7333 |
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- | 0.1144 | 9.0 | 243 | 1.7496 | 0.7111 |
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- | 0.0459 | 10.0 | 270 | 1.4601 | 0.7111 |
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- | 0.0974 | 11.0 | 297 | 2.3748 | 0.6444 |
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- | 0.0369 | 12.0 | 324 | 1.5912 | 0.8 |
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- | 0.0272 | 13.0 | 351 | 1.6618 | 0.7333 |
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- | 0.001 | 14.0 | 378 | 1.8046 | 0.7556 |
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- | 0.0003 | 15.0 | 405 | 1.8036 | 0.7556 |
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- | 0.0593 | 16.0 | 432 | 2.2624 | 0.6667 |
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- | 0.0003 | 17.0 | 459 | 1.4896 | 0.8 |
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- | 0.0001 | 18.0 | 486 | 1.6703 | 0.7333 |
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- | 0.0251 | 19.0 | 513 | 1.7506 | 0.7778 |
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- | 0.0359 | 20.0 | 540 | 1.8232 | 0.7556 |
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- | 0.071 | 21.0 | 567 | 2.0060 | 0.6444 |
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- | 0.0003 | 22.0 | 594 | 1.9743 | 0.7333 |
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- | 0.0034 | 23.0 | 621 | 1.6893 | 0.7778 |
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- | 0.0002 | 24.0 | 648 | 1.5610 | 0.7778 |
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- | 0.0001 | 25.0 | 675 | 1.6427 | 0.8 |
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- | 0.0001 | 26.0 | 702 | 1.6705 | 0.7778 |
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- | 0.0001 | 27.0 | 729 | 1.6926 | 0.8 |
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- | 0.0 | 28.0 | 756 | 1.7109 | 0.8 |
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- | 0.0 | 29.0 | 783 | 1.7284 | 0.8 |
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- | 0.0 | 30.0 | 810 | 1.7442 | 0.8 |
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- | 0.0 | 31.0 | 837 | 1.7595 | 0.8 |
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- | 0.0 | 32.0 | 864 | 1.7731 | 0.8 |
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- | 0.0 | 33.0 | 891 | 1.7860 | 0.8 |
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- | 0.0 | 34.0 | 918 | 1.7986 | 0.8 |
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- | 0.0 | 35.0 | 945 | 1.8102 | 0.8 |
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- | 0.0 | 36.0 | 972 | 1.8219 | 0.8 |
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- | 0.0 | 37.0 | 999 | 1.8331 | 0.8 |
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- | 0.0 | 38.0 | 1026 | 1.8435 | 0.8 |
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- | 0.0 | 39.0 | 1053 | 1.8534 | 0.8 |
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- | 0.0 | 40.0 | 1080 | 1.8635 | 0.8 |
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- | 0.0 | 41.0 | 1107 | 1.8728 | 0.8 |
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- | 0.0 | 42.0 | 1134 | 1.8814 | 0.8 |
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- | 0.0 | 43.0 | 1161 | 1.8891 | 0.8 |
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- | 0.0 | 44.0 | 1188 | 1.8960 | 0.8 |
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- | 0.0 | 45.0 | 1215 | 1.9019 | 0.8 |
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- | 0.0 | 46.0 | 1242 | 1.9067 | 0.8 |
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- | 0.0 | 47.0 | 1269 | 1.9100 | 0.8 |
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- | 0.0 | 48.0 | 1296 | 1.9113 | 0.8 |
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- | 0.0 | 49.0 | 1323 | 1.9113 | 0.8 |
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- | 0.0 | 50.0 | 1350 | 1.9113 | 0.8 |
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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: 0.7777777777777778
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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: 1.8006
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+ - Accuracy: 0.7778
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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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+ | 0.7439 | 1.0 | 27 | 1.0539 | 0.6444 |
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+ | 0.177 | 2.0 | 54 | 1.0125 | 0.7333 |
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+ | 0.044 | 3.0 | 81 | 1.1844 | 0.7333 |
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+ | 0.006 | 4.0 | 108 | 1.1270 | 0.7333 |
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+ | 0.0022 | 5.0 | 135 | 1.1880 | 0.7778 |
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+ | 0.0013 | 6.0 | 162 | 1.2281 | 0.7778 |
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+ | 0.001 | 7.0 | 189 | 1.2543 | 0.7778 |
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+ | 0.0006 | 8.0 | 216 | 1.2793 | 0.7778 |
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+ | 0.0005 | 9.0 | 243 | 1.3082 | 0.7778 |
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+ | 0.0004 | 10.0 | 270 | 1.3397 | 0.7778 |
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+ | 0.0004 | 11.0 | 297 | 1.3617 | 0.7778 |
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+ | 0.0003 | 12.0 | 324 | 1.3778 | 0.7778 |
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+ | 0.0002 | 13.0 | 351 | 1.3987 | 0.7778 |
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+ | 0.0002 | 14.0 | 378 | 1.4094 | 0.7778 |
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+ | 0.0002 | 15.0 | 405 | 1.4326 | 0.7778 |
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+ | 0.0002 | 16.0 | 432 | 1.4544 | 0.7778 |
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+ | 0.0001 | 17.0 | 459 | 1.4652 | 0.7778 |
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+ | 0.0001 | 18.0 | 486 | 1.4807 | 0.7778 |
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+ | 0.0001 | 19.0 | 513 | 1.5027 | 0.7778 |
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+ | 0.0001 | 20.0 | 540 | 1.5152 | 0.7778 |
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+ | 0.0001 | 21.0 | 567 | 1.5261 | 0.7778 |
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+ | 0.0001 | 22.0 | 594 | 1.5470 | 0.7778 |
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+ | 0.0001 | 23.0 | 621 | 1.5602 | 0.7778 |
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+ | 0.0001 | 24.0 | 648 | 1.5642 | 0.7778 |
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+ | 0.0001 | 25.0 | 675 | 1.5773 | 0.7778 |
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+ | 0.0 | 26.0 | 702 | 1.6051 | 0.7778 |
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+ | 0.0 | 27.0 | 729 | 1.6190 | 0.7778 |
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+ | 0.0 | 28.0 | 756 | 1.6244 | 0.7778 |
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+ | 0.0 | 29.0 | 783 | 1.6489 | 0.7778 |
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+ | 0.0 | 30.0 | 810 | 1.6490 | 0.7778 |
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+ | 0.0 | 31.0 | 837 | 1.6606 | 0.7778 |
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+ | 0.0 | 32.0 | 864 | 1.6722 | 0.7778 |
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+ | 0.0 | 33.0 | 891 | 1.6872 | 0.7778 |
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+ | 0.0 | 34.0 | 918 | 1.6956 | 0.7778 |
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+ | 0.0 | 35.0 | 945 | 1.7012 | 0.7778 |
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+ | 0.0 | 36.0 | 972 | 1.7167 | 0.7778 |
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+ | 0.0 | 37.0 | 999 | 1.7292 | 0.7778 |
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+ | 0.0 | 38.0 | 1026 | 1.7432 | 0.7778 |
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+ | 0.0 | 39.0 | 1053 | 1.7490 | 0.7778 |
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+ | 0.0 | 40.0 | 1080 | 1.7621 | 0.7778 |
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+ | 0.0 | 41.0 | 1107 | 1.7660 | 0.7778 |
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+ | 0.0 | 42.0 | 1134 | 1.7744 | 0.7778 |
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+ | 0.0 | 43.0 | 1161 | 1.7810 | 0.7778 |
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+ | 0.0 | 44.0 | 1188 | 1.7884 | 0.7778 |
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+ | 0.0 | 45.0 | 1215 | 1.7901 | 0.7778 |
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+ | 0.0 | 46.0 | 1242 | 1.7957 | 0.7778 |
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+ | 0.0 | 47.0 | 1269 | 1.7991 | 0.7778 |
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+ | 0.0 | 48.0 | 1296 | 1.8006 | 0.7778 |
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+ | 0.0 | 49.0 | 1323 | 1.8006 | 0.7778 |
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+ | 0.0 | 50.0 | 1350 | 1.8006 | 0.7778 |
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  ### Framework versions
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