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cvt-13-normal

Image Classification
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cvt
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  1. README.md +93 -107
  2. model.safetensors +1 -1
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7954939341421143
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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
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8635
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- - Accuracy: 0.7955
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  ## Model description
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@@ -53,12 +53,12 @@ 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: 0.0003
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- - train_batch_size: 128
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- - eval_batch_size: 128
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 512
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
@@ -68,106 +68,92 @@ 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 | 7 | 1.0210 | 0.7106 |
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- | 5.5642 | 2.0 | 14 | 1.0072 | 0.7097 |
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- | 5.662 | 3.0 | 21 | 1.0151 | 0.7088 |
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- | 5.662 | 4.0 | 28 | 1.0016 | 0.7140 |
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- | 5.381 | 5.0 | 35 | 1.0119 | 0.7123 |
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- | 5.3348 | 6.0 | 42 | 0.9662 | 0.7201 |
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- | 5.3348 | 7.0 | 49 | 0.9514 | 0.7262 |
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- | 5.2423 | 8.0 | 56 | 0.9589 | 0.7106 |
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- | 5.0251 | 9.0 | 63 | 0.9090 | 0.7279 |
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- | 5.0547 | 10.0 | 70 | 0.9352 | 0.7123 |
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- | 5.0547 | 11.0 | 77 | 1.0063 | 0.6993 |
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- | 4.8246 | 12.0 | 84 | 0.9191 | 0.7106 |
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- | 4.7811 | 13.0 | 91 | 0.9947 | 0.7123 |
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- | 4.7811 | 14.0 | 98 | 0.9671 | 0.7175 |
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- | 4.8234 | 15.0 | 105 | 0.9055 | 0.7236 |
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- | 4.4787 | 16.0 | 112 | 0.8838 | 0.7444 |
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- | 4.4787 | 17.0 | 119 | 0.9059 | 0.7296 |
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- | 4.39 | 18.0 | 126 | 0.8640 | 0.7461 |
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- | 4.1424 | 19.0 | 133 | 0.8661 | 0.7487 |
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- | 4.1065 | 20.0 | 140 | 0.9057 | 0.7305 |
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- | 4.1065 | 21.0 | 147 | 0.8865 | 0.7348 |
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- | 4.0844 | 22.0 | 154 | 0.8928 | 0.7392 |
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- | 3.9835 | 23.0 | 161 | 0.8675 | 0.7539 |
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- | 3.9835 | 24.0 | 168 | 0.8829 | 0.7556 |
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- | 3.8199 | 25.0 | 175 | 0.8177 | 0.7617 |
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- | 3.7898 | 26.0 | 182 | 0.8886 | 0.7461 |
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- | 3.7898 | 27.0 | 189 | 0.9395 | 0.7461 |
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- | 3.7734 | 28.0 | 196 | 0.8348 | 0.7608 |
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- | 3.7835 | 29.0 | 203 | 0.8369 | 0.7574 |
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- | 3.6414 | 30.0 | 210 | 0.8668 | 0.7660 |
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- | 3.6414 | 31.0 | 217 | 0.8909 | 0.7600 |
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- | 3.5076 | 32.0 | 224 | 0.8795 | 0.7496 |
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- | 3.5447 | 33.0 | 231 | 0.9228 | 0.7539 |
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- | 3.5447 | 34.0 | 238 | 0.8850 | 0.7522 |
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- | 3.5344 | 35.0 | 245 | 0.8585 | 0.7652 |
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- | 3.3678 | 36.0 | 252 | 0.8631 | 0.7574 |
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- | 3.3678 | 37.0 | 259 | 0.8676 | 0.7704 |
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- | 3.4061 | 38.0 | 266 | 0.9131 | 0.7617 |
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- | 3.3177 | 39.0 | 273 | 0.8631 | 0.7678 |
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- | 3.2767 | 40.0 | 280 | 0.8802 | 0.7643 |
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- | 3.2767 | 41.0 | 287 | 0.8518 | 0.7678 |
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- | 3.1992 | 42.0 | 294 | 0.9232 | 0.7574 |
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- | 3.2743 | 43.0 | 301 | 0.9306 | 0.7522 |
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- | 3.2743 | 44.0 | 308 | 0.8420 | 0.7756 |
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- | 3.1704 | 45.0 | 315 | 0.8802 | 0.7565 |
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- | 3.2466 | 46.0 | 322 | 0.8782 | 0.7678 |
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- | 3.2466 | 47.0 | 329 | 0.8444 | 0.7747 |
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- | 3.0879 | 48.0 | 336 | 0.8579 | 0.7695 |
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- | 3.1677 | 49.0 | 343 | 0.8584 | 0.7712 |
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- | 3.0965 | 50.0 | 350 | 0.8401 | 0.7756 |
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- | 3.0965 | 51.0 | 357 | 0.8724 | 0.7652 |
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- | 3.0611 | 52.0 | 364 | 0.8638 | 0.7808 |
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- | 3.0204 | 53.0 | 371 | 0.9167 | 0.7660 |
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- | 3.0204 | 54.0 | 378 | 0.8322 | 0.7738 |
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- | 2.9704 | 55.0 | 385 | 0.8577 | 0.7643 |
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- | 2.939 | 56.0 | 392 | 0.8297 | 0.7860 |
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- | 2.939 | 57.0 | 399 | 0.8746 | 0.7686 |
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- | 3.0341 | 58.0 | 406 | 0.8620 | 0.7825 |
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- | 2.8997 | 59.0 | 413 | 0.8835 | 0.7574 |
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- | 3.0187 | 60.0 | 420 | 0.9018 | 0.7695 |
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- | 3.0187 | 61.0 | 427 | 0.8940 | 0.7773 |
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- | 2.9316 | 62.0 | 434 | 0.8859 | 0.7712 |
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- | 2.8746 | 63.0 | 441 | 0.8661 | 0.7764 |
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- | 2.8746 | 64.0 | 448 | 0.8916 | 0.7712 |
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- | 2.817 | 65.0 | 455 | 0.8645 | 0.7782 |
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- | 2.7593 | 66.0 | 462 | 0.8829 | 0.7686 |
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- | 2.7593 | 67.0 | 469 | 0.8883 | 0.7790 |
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- | 2.9212 | 68.0 | 476 | 0.8507 | 0.7825 |
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- | 2.8659 | 69.0 | 483 | 0.8554 | 0.7877 |
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- | 2.9068 | 70.0 | 490 | 0.8813 | 0.7764 |
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- | 2.9068 | 71.0 | 497 | 0.8555 | 0.7860 |
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- | 2.8334 | 72.0 | 504 | 0.8666 | 0.7790 |
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- | 2.7322 | 73.0 | 511 | 0.8682 | 0.7825 |
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- | 2.7322 | 74.0 | 518 | 0.8816 | 0.7886 |
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- | 2.8548 | 75.0 | 525 | 0.8523 | 0.7903 |
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- | 2.8696 | 76.0 | 532 | 0.8509 | 0.7894 |
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- | 2.8696 | 77.0 | 539 | 0.8683 | 0.7808 |
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- | 2.6439 | 78.0 | 546 | 0.8607 | 0.7877 |
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- | 2.9039 | 79.0 | 553 | 0.8698 | 0.7842 |
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- | 2.6338 | 80.0 | 560 | 0.8718 | 0.7877 |
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- | 2.6338 | 81.0 | 567 | 0.8371 | 0.7903 |
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- | 2.7271 | 82.0 | 574 | 0.8427 | 0.7929 |
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- | 2.7555 | 83.0 | 581 | 0.8622 | 0.7938 |
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- | 2.7555 | 84.0 | 588 | 0.8769 | 0.7860 |
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- | 2.7702 | 85.0 | 595 | 0.8844 | 0.7860 |
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- | 2.8678 | 86.0 | 602 | 0.8882 | 0.7825 |
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- | 2.8678 | 87.0 | 609 | 0.8716 | 0.7825 |
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- | 2.6334 | 88.0 | 616 | 0.8782 | 0.7782 |
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- | 2.7782 | 89.0 | 623 | 0.8752 | 0.7808 |
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- | 2.5527 | 90.0 | 630 | 0.8675 | 0.7808 |
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- | 2.5527 | 91.0 | 637 | 0.8735 | 0.7842 |
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- | 2.6812 | 92.0 | 644 | 0.8650 | 0.7886 |
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- | 2.6167 | 93.0 | 651 | 0.8531 | 0.7946 |
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- | 2.6167 | 94.0 | 658 | 0.8699 | 0.7868 |
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- | 2.6553 | 95.0 | 665 | 0.8667 | 0.7894 |
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- | 2.7758 | 96.0 | 672 | 0.8650 | 0.7920 |
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- | 2.7758 | 97.0 | 679 | 0.8685 | 0.7903 |
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- | 2.6592 | 98.0 | 686 | 0.8592 | 0.7886 |
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- | 2.5202 | 99.0 | 693 | 0.8745 | 0.7894 |
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- | 2.6577 | 100.0 | 700 | 0.8635 | 0.7955 |
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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: 0.7790294627383015
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  ---
28
 
29
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
33
 
34
  This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset.
35
  It achieves the following results on the evaluation set:
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+ - Loss: 1.0613
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+ - Accuracy: 0.7790
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39
  ## Model description
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53
  ### Training hyperparameters
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55
  The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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+ - train_batch_size: 142
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+ - eval_batch_size: 142
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  - seed: 42
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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 568
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 7 | 0.8652 | 0.7946 |
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+ | 2.3799 | 2.0 | 14 | 0.8683 | 0.7912 |
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+ | 2.4491 | 3.0 | 21 | 0.8807 | 0.7825 |
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+ | 2.4491 | 4.0 | 28 | 0.9120 | 0.7851 |
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+ | 2.3011 | 5.0 | 35 | 0.9865 | 0.7565 |
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+ | 2.5444 | 6.0 | 42 | 0.9863 | 0.7643 |
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+ | 2.5444 | 7.0 | 49 | 1.1580 | 0.7513 |
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+ | 2.4127 | 8.0 | 56 | 1.1091 | 0.7383 |
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+ | 2.8757 | 9.0 | 63 | 1.0644 | 0.7496 |
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+ | 2.5231 | 10.0 | 70 | 1.0888 | 0.7400 |
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+ | 2.5231 | 11.0 | 77 | 1.0668 | 0.7548 |
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+ | 2.7538 | 12.0 | 84 | 1.0946 | 0.7435 |
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+ | 2.7032 | 13.0 | 91 | 1.0676 | 0.7608 |
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+ | 2.7032 | 14.0 | 98 | 1.0409 | 0.7426 |
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+ | 2.4581 | 15.0 | 105 | 1.0679 | 0.7548 |
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+ | 2.7023 | 16.0 | 112 | 1.0129 | 0.7487 |
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+ | 2.7023 | 17.0 | 119 | 1.1501 | 0.7366 |
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+ | 2.5456 | 18.0 | 126 | 1.0452 | 0.7426 |
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+ | 2.7061 | 19.0 | 133 | 1.0034 | 0.7565 |
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+ | 2.3491 | 20.0 | 140 | 1.0389 | 0.7574 |
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+ | 2.3491 | 21.0 | 147 | 0.9999 | 0.7782 |
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+ | 2.4926 | 22.0 | 154 | 1.0131 | 0.7652 |
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+ | 2.5111 | 23.0 | 161 | 1.0940 | 0.7340 |
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+ | 2.5111 | 24.0 | 168 | 1.0786 | 0.7582 |
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+ | 2.3443 | 25.0 | 175 | 1.0768 | 0.7617 |
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+ | 2.5738 | 26.0 | 182 | 0.9781 | 0.7782 |
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+ | 2.5738 | 27.0 | 189 | 0.9955 | 0.7574 |
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+ | 2.3528 | 28.0 | 196 | 1.0117 | 0.7669 |
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+ | 2.599 | 29.0 | 203 | 1.0806 | 0.7660 |
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+ | 2.3279 | 30.0 | 210 | 1.0101 | 0.7738 |
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+ | 2.3279 | 31.0 | 217 | 1.0981 | 0.7617 |
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+ | 2.5649 | 32.0 | 224 | 1.0185 | 0.7782 |
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+ | 2.5432 | 33.0 | 231 | 1.1070 | 0.7591 |
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+ | 2.5432 | 34.0 | 238 | 1.0705 | 0.7626 |
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+ | 2.3521 | 35.0 | 245 | 1.0749 | 0.7574 |
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+ | 2.5948 | 36.0 | 252 | 1.0508 | 0.7626 |
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+ | 2.5948 | 37.0 | 259 | 1.0374 | 0.7712 |
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+ | 2.3305 | 38.0 | 266 | 1.0249 | 0.7643 |
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+ | 2.4833 | 39.0 | 273 | 1.0345 | 0.7712 |
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+ | 2.1504 | 40.0 | 280 | 1.0252 | 0.7617 |
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+ | 2.1504 | 41.0 | 287 | 1.0361 | 0.7574 |
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+ | 2.4083 | 42.0 | 294 | 0.9939 | 0.7678 |
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+ | 2.37 | 43.0 | 301 | 1.0186 | 0.7695 |
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+ | 2.37 | 44.0 | 308 | 1.0861 | 0.7643 |
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+ | 2.2043 | 45.0 | 315 | 1.0182 | 0.7643 |
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+ | 2.3554 | 46.0 | 322 | 1.0584 | 0.7539 |
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+ | 2.3554 | 47.0 | 329 | 1.0541 | 0.7617 |
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+ | 2.1541 | 48.0 | 336 | 1.0967 | 0.7686 |
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+ | 2.3739 | 49.0 | 343 | 1.1266 | 0.7721 |
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+ | 2.1028 | 50.0 | 350 | 1.1116 | 0.7652 |
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+ | 2.1028 | 51.0 | 357 | 1.0804 | 0.7643 |
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+ | 2.3381 | 52.0 | 364 | 1.1142 | 0.7556 |
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+ | 2.2902 | 53.0 | 371 | 1.1135 | 0.7652 |
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+ | 2.2902 | 54.0 | 378 | 1.1024 | 0.7461 |
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+ | 2.2452 | 55.0 | 385 | 1.0722 | 0.7626 |
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+ | 2.4121 | 56.0 | 392 | 1.1089 | 0.7704 |
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+ | 2.4121 | 57.0 | 399 | 1.0923 | 0.7548 |
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+ | 2.2067 | 58.0 | 406 | 1.0811 | 0.7591 |
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+ | 2.3894 | 59.0 | 413 | 1.1097 | 0.7634 |
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+ | 2.2188 | 60.0 | 420 | 1.0988 | 0.7643 |
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+ | 2.2188 | 61.0 | 427 | 1.0558 | 0.7686 |
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+ | 2.2859 | 62.0 | 434 | 1.0569 | 0.7695 |
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+ | 2.2293 | 63.0 | 441 | 1.1053 | 0.7643 |
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+ | 2.2293 | 64.0 | 448 | 1.0962 | 0.7652 |
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+ | 2.136 | 65.0 | 455 | 1.0505 | 0.7756 |
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+ | 2.2507 | 66.0 | 462 | 1.0425 | 0.7799 |
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+ | 2.2507 | 67.0 | 469 | 1.0703 | 0.7756 |
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+ | 2.0269 | 68.0 | 476 | 1.0826 | 0.7695 |
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+ | 2.2972 | 69.0 | 483 | 1.0569 | 0.7747 |
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+ | 2.0192 | 70.0 | 490 | 1.0773 | 0.7695 |
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+ | 2.0192 | 71.0 | 497 | 1.1000 | 0.7669 |
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+ | 2.3668 | 72.0 | 504 | 1.1048 | 0.7712 |
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+ | 2.1285 | 73.0 | 511 | 1.0883 | 0.7712 |
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+ | 2.1285 | 74.0 | 518 | 1.0893 | 0.7738 |
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+ | 2.0487 | 75.0 | 525 | 1.0644 | 0.7799 |
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+ | 2.2508 | 76.0 | 532 | 1.0686 | 0.7764 |
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+ | 2.2508 | 77.0 | 539 | 1.0759 | 0.7764 |
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+ | 2.0141 | 78.0 | 546 | 1.0673 | 0.7756 |
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+ | 2.1662 | 79.0 | 553 | 1.0610 | 0.7842 |
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+ | 2.0567 | 80.0 | 560 | 1.0571 | 0.7851 |
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+ | 2.0567 | 81.0 | 567 | 1.0682 | 0.7799 |
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+ | 2.2602 | 82.0 | 574 | 1.0700 | 0.7782 |
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+ | 2.3018 | 83.0 | 581 | 1.0703 | 0.7790 |
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+ | 2.3018 | 84.0 | 588 | 1.0597 | 0.7825 |
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+ | 2.0309 | 85.0 | 595 | 1.0560 | 0.7825 |
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+ | 2.108 | 85.8 | 600 | 1.0613 | 0.7790 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
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  size 78658852
 
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