ScCvT_K-fold / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - f1
  - accuracy
model-index:
  - name: ScCvT_fold_5
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: F1
            type: f1
            value: 0.909963393596575
          - name: Accuracy
            type: accuracy
            value: 0.9102764736567553

ScCvT_fold_5

This model is a fine-tuned version of microsoft/cvt-13 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3026
  • F1: 0.9100
  • Roc Auc: 0.9850
  • Accuracy: 0.9103

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 65

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc Accuracy
1.8858 1.0 60 1.8336 0.3756 0.6770 0.3062
1.7573 2.0 120 1.5748 0.5904 0.8013 0.5342
1.6042 3.0 180 1.2448 0.6648 0.8782 0.6270
1.4011 4.0 240 1.0728 0.6856 0.9162 0.6458
1.3588 5.0 300 0.8680 0.7432 0.9329 0.7131
1.2291 6.0 360 0.8431 0.7378 0.9400 0.7053
1.145 7.0 420 0.8501 0.7355 0.9453 0.6974
1.0652 8.0 480 0.7471 0.7559 0.9533 0.7251
1.0174 9.0 540 0.5592 0.8140 0.9634 0.8002
0.892 10.0 600 0.6785 0.7726 0.9614 0.7480
0.8584 11.0 660 0.5690 0.8088 0.9676 0.7898
0.8662 12.0 720 0.6049 0.7911 0.9696 0.7679
0.9131 13.0 780 0.4984 0.8295 0.9717 0.8179
0.8616 14.0 840 0.4755 0.8301 0.9722 0.8190
0.8398 15.0 900 0.5121 0.8237 0.9721 0.8101
0.6746 16.0 960 0.4823 0.8322 0.9742 0.8185
0.779 17.0 1020 0.5121 0.8193 0.9749 0.8002
0.7436 18.0 1080 0.4911 0.8270 0.9740 0.8153
0.7586 19.0 1140 0.4376 0.8439 0.9769 0.8346
0.688 20.0 1200 0.4732 0.8377 0.9784 0.8247
0.8294 21.0 1260 0.4889 0.8323 0.9777 0.8138
0.7451 22.0 1320 0.4015 0.8605 0.9787 0.8545
0.708 23.0 1380 0.3818 0.8677 0.9800 0.8612
0.6112 24.0 1440 0.4229 0.8651 0.9781 0.8565
0.6936 25.0 1500 0.3678 0.8714 0.9809 0.8670
0.6486 26.0 1560 0.3565 0.8722 0.9807 0.8685
0.6078 27.0 1620 0.3489 0.8760 0.9815 0.8722
0.6513 28.0 1680 0.3546 0.8774 0.9821 0.8727
0.6562 29.0 1740 0.3420 0.8816 0.9817 0.8774
0.6561 30.0 1800 0.3649 0.8745 0.9818 0.8691
0.6056 31.0 1860 0.3692 0.8730 0.9823 0.8670
0.5927 32.0 1920 0.3584 0.8825 0.9822 0.8779
0.6021 33.0 1980 0.3345 0.8870 0.9821 0.8842
0.6052 34.0 2040 0.3388 0.8887 0.9820 0.8868
0.6026 35.0 2100 0.3161 0.8938 0.9831 0.8925
0.5735 36.0 2160 0.3324 0.8952 0.9825 0.8957
0.6058 37.0 2220 0.3262 0.8932 0.9829 0.8905
0.5648 38.0 2280 0.3243 0.8931 0.9834 0.8899
0.6248 39.0 2340 0.3499 0.8825 0.9837 0.8769
0.4926 40.0 2400 0.3066 0.9045 0.9837 0.9045
0.5341 41.0 2460 0.3037 0.9042 0.9835 0.9061
0.5215 42.0 2520 0.3193 0.8968 0.9832 0.8967
0.5892 43.0 2580 0.3276 0.8910 0.9837 0.8884
0.559 44.0 2640 0.3129 0.9006 0.9843 0.8988
0.5306 45.0 2700 0.3137 0.9024 0.9839 0.9035
0.4789 46.0 2760 0.3128 0.8984 0.9842 0.8972
0.5518 47.0 2820 0.3117 0.9057 0.9838 0.9051
0.5201 48.0 2880 0.3110 0.9023 0.9842 0.9014
0.5698 49.0 2940 0.3031 0.9074 0.9841 0.9071
0.5227 50.0 3000 0.3343 0.9007 0.9834 0.8988
0.5416 51.0 3060 0.3117 0.9078 0.9837 0.9082
0.5882 52.0 3120 0.3132 0.9049 0.9835 0.9051
0.4286 53.0 3180 0.3133 0.9064 0.9837 0.9066
0.5278 54.0 3240 0.3050 0.9080 0.9847 0.9077
0.586 55.0 3300 0.3063 0.9065 0.9842 0.9082
0.4708 56.0 3360 0.3119 0.9058 0.9840 0.9056
0.5512 57.0 3420 0.3140 0.9023 0.9842 0.9014
0.5535 58.0 3480 0.3079 0.9046 0.9847 0.9045
0.4817 59.0 3540 0.3077 0.9061 0.9845 0.9061
0.5381 60.0 3600 0.3122 0.9015 0.9848 0.9009
0.6305 61.0 3660 0.3026 0.9100 0.9850 0.9103
0.4658 62.0 3720 0.3044 0.9093 0.9851 0.9092
0.4873 63.0 3780 0.3076 0.9074 0.9849 0.9071
0.5791 64.0 3840 0.3045 0.9077 0.9850 0.9077
0.5871 65.0 3900 0.3028 0.9076 0.9852 0.9071

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3