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hushem_5x_deit_small_rms_0001_fold4

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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: 0.9761904761904762
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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: 0.2776
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- - Accuracy: 0.9762
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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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- | 1.547 | 1.0 | 28 | 1.3156 | 0.3571 |
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- | 1.0708 | 2.0 | 56 | 0.7305 | 0.7381 |
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- | 0.4592 | 3.0 | 84 | 0.4363 | 0.7857 |
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- | 0.2242 | 4.0 | 112 | 0.3032 | 0.9048 |
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- | 0.1723 | 5.0 | 140 | 0.4398 | 0.9048 |
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- | 0.0273 | 6.0 | 168 | 0.1828 | 0.9286 |
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- | 0.0074 | 7.0 | 196 | 0.2243 | 0.9286 |
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- | 0.0246 | 8.0 | 224 | 0.6869 | 0.8095 |
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- | 0.0271 | 9.0 | 252 | 0.5189 | 0.8810 |
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- | 0.0003 | 10.0 | 280 | 0.4075 | 0.9048 |
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- | 0.0001 | 11.0 | 308 | 0.3572 | 0.9286 |
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- | 0.0001 | 12.0 | 336 | 0.3487 | 0.9286 |
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- | 0.0001 | 13.0 | 364 | 0.3437 | 0.9286 |
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- | 0.0001 | 14.0 | 392 | 0.3375 | 0.9286 |
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- | 0.0001 | 15.0 | 420 | 0.3312 | 0.9286 |
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- | 0.0001 | 16.0 | 448 | 0.3266 | 0.9286 |
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- | 0.0 | 17.0 | 476 | 0.3232 | 0.9286 |
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- | 0.0 | 18.0 | 504 | 0.3215 | 0.9286 |
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- | 0.0 | 19.0 | 532 | 0.3147 | 0.9286 |
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- | 0.0 | 20.0 | 560 | 0.3112 | 0.9286 |
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- | 0.0 | 21.0 | 588 | 0.3078 | 0.9286 |
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- | 0.0 | 22.0 | 616 | 0.3039 | 0.9524 |
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- | 0.0 | 23.0 | 644 | 0.3017 | 0.9524 |
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- | 0.0 | 24.0 | 672 | 0.2991 | 0.9286 |
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- | 0.0 | 25.0 | 700 | 0.2971 | 0.9524 |
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- | 0.0 | 26.0 | 728 | 0.2950 | 0.9524 |
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- | 0.0 | 27.0 | 756 | 0.2933 | 0.9524 |
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- | 0.0 | 28.0 | 784 | 0.2903 | 0.9524 |
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- | 0.0 | 29.0 | 812 | 0.2892 | 0.9524 |
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- | 0.0 | 30.0 | 840 | 0.2867 | 0.9524 |
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- | 0.0 | 31.0 | 868 | 0.2851 | 0.9524 |
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- | 0.0 | 32.0 | 896 | 0.2853 | 0.9524 |
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- | 0.0 | 33.0 | 924 | 0.2820 | 0.9524 |
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- | 0.0 | 34.0 | 952 | 0.2802 | 0.9524 |
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- | 0.0 | 35.0 | 980 | 0.2774 | 0.9524 |
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- | 0.0 | 36.0 | 1008 | 0.2771 | 0.9524 |
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- | 0.0 | 37.0 | 1036 | 0.2772 | 0.9524 |
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- | 0.0 | 38.0 | 1064 | 0.2772 | 0.9524 |
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- | 0.0 | 39.0 | 1092 | 0.2769 | 0.9524 |
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- | 0.0 | 40.0 | 1120 | 0.2766 | 0.9524 |
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- | 0.0 | 41.0 | 1148 | 0.2767 | 0.9524 |
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- | 0.0 | 42.0 | 1176 | 0.2776 | 0.9524 |
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- | 0.0 | 43.0 | 1204 | 0.2777 | 0.9524 |
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- | 0.0 | 44.0 | 1232 | 0.2779 | 0.9524 |
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- | 0.0 | 45.0 | 1260 | 0.2777 | 0.9524 |
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- | 0.0 | 46.0 | 1288 | 0.2776 | 0.9524 |
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- | 0.0 | 47.0 | 1316 | 0.2776 | 0.9524 |
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- | 0.0 | 48.0 | 1344 | 0.2776 | 0.9762 |
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- | 0.0 | 49.0 | 1372 | 0.2776 | 0.9762 |
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- | 0.0 | 50.0 | 1400 | 0.2776 | 0.9762 |
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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: 0.9285714285714286
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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: 0.3023
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+ - Accuracy: 0.9286
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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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+ | 0.7855 | 1.0 | 28 | 1.0573 | 0.5952 |
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+ | 0.2045 | 2.0 | 56 | 0.5015 | 0.8571 |
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+ | 0.0737 | 3.0 | 84 | 0.4338 | 0.8571 |
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+ | 0.0109 | 4.0 | 112 | 0.2957 | 0.9048 |
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+ | 0.0022 | 5.0 | 140 | 0.2261 | 0.9286 |
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+ | 0.0014 | 6.0 | 168 | 0.2506 | 0.9286 |
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+ | 0.0008 | 7.0 | 196 | 0.2522 | 0.9286 |
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+ | 0.0007 | 8.0 | 224 | 0.2446 | 0.9286 |
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+ | 0.0005 | 9.0 | 252 | 0.2487 | 0.9286 |
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+ | 0.0004 | 10.0 | 280 | 0.2517 | 0.9286 |
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+ | 0.0003 | 11.0 | 308 | 0.2553 | 0.9286 |
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+ | 0.0003 | 12.0 | 336 | 0.2555 | 0.9286 |
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+ | 0.0002 | 13.0 | 364 | 0.2681 | 0.9286 |
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+ | 0.0002 | 14.0 | 392 | 0.2586 | 0.9286 |
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+ | 0.0002 | 15.0 | 420 | 0.2637 | 0.9286 |
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+ | 0.0001 | 16.0 | 448 | 0.2681 | 0.9286 |
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+ | 0.0001 | 17.0 | 476 | 0.2601 | 0.9286 |
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+ | 0.0001 | 18.0 | 504 | 0.2694 | 0.9286 |
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+ | 0.0001 | 19.0 | 532 | 0.2689 | 0.9286 |
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+ | 0.0001 | 20.0 | 560 | 0.2671 | 0.9286 |
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+ | 0.0001 | 21.0 | 588 | 0.2727 | 0.9286 |
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+ | 0.0001 | 22.0 | 616 | 0.2647 | 0.9286 |
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+ | 0.0001 | 23.0 | 644 | 0.2798 | 0.9286 |
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+ | 0.0001 | 24.0 | 672 | 0.2721 | 0.9286 |
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+ | 0.0 | 25.0 | 700 | 0.2708 | 0.9524 |
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+ | 0.0 | 26.0 | 728 | 0.2677 | 0.9524 |
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+ | 0.0 | 27.0 | 756 | 0.2786 | 0.9524 |
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+ | 0.0 | 28.0 | 784 | 0.2735 | 0.9524 |
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+ | 0.0 | 29.0 | 812 | 0.2856 | 0.9286 |
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+ | 0.0 | 30.0 | 840 | 0.2835 | 0.9524 |
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+ | 0.0 | 31.0 | 868 | 0.2832 | 0.9524 |
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+ | 0.0 | 32.0 | 896 | 0.2866 | 0.9524 |
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+ | 0.0 | 33.0 | 924 | 0.2846 | 0.9524 |
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+ | 0.0 | 34.0 | 952 | 0.2883 | 0.9524 |
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+ | 0.0 | 35.0 | 980 | 0.2826 | 0.9286 |
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+ | 0.0 | 36.0 | 1008 | 0.2875 | 0.9524 |
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+ | 0.0 | 37.0 | 1036 | 0.2891 | 0.9524 |
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+ | 0.0 | 38.0 | 1064 | 0.2916 | 0.9524 |
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+ | 0.0 | 39.0 | 1092 | 0.2920 | 0.9524 |
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+ | 0.0 | 40.0 | 1120 | 0.2941 | 0.9524 |
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+ | 0.0 | 41.0 | 1148 | 0.2956 | 0.9524 |
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+ | 0.0 | 42.0 | 1176 | 0.2982 | 0.9286 |
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+ | 0.0 | 43.0 | 1204 | 0.2993 | 0.9286 |
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+ | 0.0 | 44.0 | 1232 | 0.2997 | 0.9286 |
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+ | 0.0 | 45.0 | 1260 | 0.3014 | 0.9286 |
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+ | 0.0 | 46.0 | 1288 | 0.3035 | 0.9286 |
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+ | 0.0 | 47.0 | 1316 | 0.3025 | 0.9286 |
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+ | 0.0 | 48.0 | 1344 | 0.3023 | 0.9286 |
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+ | 0.0 | 49.0 | 1372 | 0.3023 | 0.9286 |
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+ | 0.0 | 50.0 | 1400 | 0.3023 | 0.9286 |
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  ### Framework versions
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