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hushem_1x_deit_tiny_adamax_001_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: 0.627906976744186
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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-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.8954
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- - Accuracy: 0.6279
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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: 0.001
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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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- | No log | 1.0 | 6 | 1.3703 | 0.2558 |
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- | 1.9279 | 2.0 | 12 | 1.2966 | 0.3953 |
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- | 1.9279 | 3.0 | 18 | 1.5490 | 0.3256 |
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- | 1.3451 | 4.0 | 24 | 1.3082 | 0.4186 |
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- | 1.2763 | 5.0 | 30 | 1.4000 | 0.3023 |
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- | 1.2763 | 6.0 | 36 | 1.3783 | 0.3488 |
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- | 1.1541 | 7.0 | 42 | 1.2878 | 0.3953 |
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- | 1.1541 | 8.0 | 48 | 1.2528 | 0.4651 |
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- | 1.0831 | 9.0 | 54 | 1.2761 | 0.4884 |
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- | 1.0032 | 10.0 | 60 | 0.9439 | 0.6279 |
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- | 1.0032 | 11.0 | 66 | 1.9597 | 0.3256 |
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- | 1.0649 | 12.0 | 72 | 1.3501 | 0.4651 |
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- | 1.0649 | 13.0 | 78 | 1.2845 | 0.6279 |
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- | 0.8485 | 14.0 | 84 | 1.2102 | 0.5814 |
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- | 0.7758 | 15.0 | 90 | 1.5993 | 0.4651 |
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- | 0.7758 | 16.0 | 96 | 1.1744 | 0.6279 |
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- | 0.5906 | 17.0 | 102 | 1.9493 | 0.4884 |
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- | 0.5906 | 18.0 | 108 | 1.3370 | 0.5581 |
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- | 0.5433 | 19.0 | 114 | 1.8704 | 0.5814 |
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- | 0.4053 | 20.0 | 120 | 2.3449 | 0.6047 |
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- | 0.4053 | 21.0 | 126 | 2.8071 | 0.4651 |
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- | 0.6321 | 22.0 | 132 | 1.8750 | 0.5814 |
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- | 0.6321 | 23.0 | 138 | 1.9591 | 0.5814 |
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- | 0.2883 | 24.0 | 144 | 2.0517 | 0.6744 |
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- | 0.2248 | 25.0 | 150 | 2.2716 | 0.5581 |
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- | 0.2248 | 26.0 | 156 | 2.5758 | 0.5581 |
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- | 0.0908 | 27.0 | 162 | 2.4971 | 0.5814 |
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- | 0.0908 | 28.0 | 168 | 2.2990 | 0.6512 |
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- | 0.0607 | 29.0 | 174 | 2.2806 | 0.6977 |
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- | 0.0385 | 30.0 | 180 | 2.4187 | 0.6279 |
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- | 0.0385 | 31.0 | 186 | 2.4113 | 0.6744 |
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- | 0.0085 | 32.0 | 192 | 2.4630 | 0.6512 |
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- | 0.0085 | 33.0 | 198 | 2.7214 | 0.6279 |
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- | 0.004 | 34.0 | 204 | 2.8415 | 0.6047 |
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- | 0.0007 | 35.0 | 210 | 2.8858 | 0.6047 |
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- | 0.0007 | 36.0 | 216 | 2.8956 | 0.6279 |
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- | 0.0005 | 37.0 | 222 | 2.8935 | 0.6279 |
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- | 0.0005 | 38.0 | 228 | 2.8908 | 0.6279 |
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- | 0.0004 | 39.0 | 234 | 2.8922 | 0.6279 |
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- | 0.0003 | 40.0 | 240 | 2.8936 | 0.6279 |
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- | 0.0003 | 41.0 | 246 | 2.8951 | 0.6279 |
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- | 0.0003 | 42.0 | 252 | 2.8954 | 0.6279 |
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- | 0.0003 | 43.0 | 258 | 2.8954 | 0.6279 |
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- | 0.0003 | 44.0 | 264 | 2.8954 | 0.6279 |
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- | 0.0003 | 45.0 | 270 | 2.8954 | 0.6279 |
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- | 0.0003 | 46.0 | 276 | 2.8954 | 0.6279 |
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- | 0.0003 | 47.0 | 282 | 2.8954 | 0.6279 |
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- | 0.0003 | 48.0 | 288 | 2.8954 | 0.6279 |
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- | 0.0003 | 49.0 | 294 | 2.8954 | 0.6279 |
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- | 0.0003 | 50.0 | 300 | 2.8954 | 0.6279 |
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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: 0.8372093023255814
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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
 
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  This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5538
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+ - Accuracy: 0.8372
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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: 0.0001
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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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+ | No log | 1.0 | 6 | 1.3426 | 0.4419 |
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+ | 1.3195 | 2.0 | 12 | 1.0931 | 0.5116 |
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+ | 1.3195 | 3.0 | 18 | 0.8535 | 0.6512 |
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+ | 0.6419 | 4.0 | 24 | 0.9249 | 0.6279 |
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+ | 0.325 | 5.0 | 30 | 0.7057 | 0.7674 |
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+ | 0.325 | 6.0 | 36 | 0.5831 | 0.7674 |
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+ | 0.0848 | 7.0 | 42 | 0.6810 | 0.7907 |
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+ | 0.0848 | 8.0 | 48 | 0.5917 | 0.7674 |
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+ | 0.0193 | 9.0 | 54 | 0.6267 | 0.8140 |
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+ | 0.0077 | 10.0 | 60 | 0.4330 | 0.8372 |
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+ | 0.0077 | 11.0 | 66 | 0.5195 | 0.8372 |
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+ | 0.0032 | 12.0 | 72 | 0.6710 | 0.7907 |
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+ | 0.0032 | 13.0 | 78 | 0.6980 | 0.8372 |
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+ | 0.0012 | 14.0 | 84 | 0.5701 | 0.8372 |
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+ | 0.0006 | 15.0 | 90 | 0.5278 | 0.8605 |
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+ | 0.0006 | 16.0 | 96 | 0.5226 | 0.8372 |
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+ | 0.0005 | 17.0 | 102 | 0.5245 | 0.8605 |
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+ | 0.0005 | 18.0 | 108 | 0.5277 | 0.8605 |
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+ | 0.0004 | 19.0 | 114 | 0.5338 | 0.8372 |
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+ | 0.0003 | 20.0 | 120 | 0.5401 | 0.8372 |
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+ | 0.0003 | 21.0 | 126 | 0.5445 | 0.8372 |
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+ | 0.0003 | 22.0 | 132 | 0.5461 | 0.8372 |
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+ | 0.0003 | 23.0 | 138 | 0.5481 | 0.8372 |
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+ | 0.0003 | 24.0 | 144 | 0.5486 | 0.8372 |
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+ | 0.0003 | 25.0 | 150 | 0.5495 | 0.8372 |
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+ | 0.0003 | 26.0 | 156 | 0.5492 | 0.8372 |
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+ | 0.0002 | 27.0 | 162 | 0.5497 | 0.8372 |
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+ | 0.0002 | 28.0 | 168 | 0.5490 | 0.8372 |
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+ | 0.0002 | 29.0 | 174 | 0.5497 | 0.8372 |
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+ | 0.0002 | 30.0 | 180 | 0.5498 | 0.8372 |
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+ | 0.0002 | 31.0 | 186 | 0.5499 | 0.8372 |
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+ | 0.0002 | 32.0 | 192 | 0.5503 | 0.8372 |
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+ | 0.0002 | 33.0 | 198 | 0.5508 | 0.8372 |
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+ | 0.0002 | 34.0 | 204 | 0.5520 | 0.8372 |
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+ | 0.0002 | 35.0 | 210 | 0.5527 | 0.8372 |
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+ | 0.0002 | 36.0 | 216 | 0.5529 | 0.8372 |
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+ | 0.0002 | 37.0 | 222 | 0.5532 | 0.8372 |
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+ | 0.0002 | 38.0 | 228 | 0.5534 | 0.8372 |
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+ | 0.0002 | 39.0 | 234 | 0.5536 | 0.8372 |
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+ | 0.0002 | 40.0 | 240 | 0.5537 | 0.8372 |
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+ | 0.0002 | 41.0 | 246 | 0.5538 | 0.8372 |
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+ | 0.0002 | 42.0 | 252 | 0.5538 | 0.8372 |
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+ | 0.0002 | 43.0 | 258 | 0.5538 | 0.8372 |
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+ | 0.0002 | 44.0 | 264 | 0.5538 | 0.8372 |
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+ | 0.0002 | 45.0 | 270 | 0.5538 | 0.8372 |
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+ | 0.0002 | 46.0 | 276 | 0.5538 | 0.8372 |
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+ | 0.0002 | 47.0 | 282 | 0.5538 | 0.8372 |
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+ | 0.0002 | 48.0 | 288 | 0.5538 | 0.8372 |
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+ | 0.0002 | 49.0 | 294 | 0.5538 | 0.8372 |
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+ | 0.0002 | 50.0 | 300 | 0.5538 | 0.8372 |
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  ### Framework versions
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