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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: ScCvT_fold_5 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.909963393596575 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9102764736567553 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# ScCvT_fold_5 |
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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: 0.3026 |
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- F1: 0.9100 |
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- Roc Auc: 0.9850 |
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- Accuracy: 0.9103 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 65 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 1.8858 | 1.0 | 60 | 1.8336 | 0.3756 | 0.6770 | 0.3062 | |
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| 1.7573 | 2.0 | 120 | 1.5748 | 0.5904 | 0.8013 | 0.5342 | |
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| 1.6042 | 3.0 | 180 | 1.2448 | 0.6648 | 0.8782 | 0.6270 | |
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| 1.4011 | 4.0 | 240 | 1.0728 | 0.6856 | 0.9162 | 0.6458 | |
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| 1.3588 | 5.0 | 300 | 0.8680 | 0.7432 | 0.9329 | 0.7131 | |
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| 1.2291 | 6.0 | 360 | 0.8431 | 0.7378 | 0.9400 | 0.7053 | |
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| 1.145 | 7.0 | 420 | 0.8501 | 0.7355 | 0.9453 | 0.6974 | |
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| 1.0652 | 8.0 | 480 | 0.7471 | 0.7559 | 0.9533 | 0.7251 | |
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| 1.0174 | 9.0 | 540 | 0.5592 | 0.8140 | 0.9634 | 0.8002 | |
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| 0.892 | 10.0 | 600 | 0.6785 | 0.7726 | 0.9614 | 0.7480 | |
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| 0.8584 | 11.0 | 660 | 0.5690 | 0.8088 | 0.9676 | 0.7898 | |
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| 0.8662 | 12.0 | 720 | 0.6049 | 0.7911 | 0.9696 | 0.7679 | |
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| 0.9131 | 13.0 | 780 | 0.4984 | 0.8295 | 0.9717 | 0.8179 | |
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| 0.8616 | 14.0 | 840 | 0.4755 | 0.8301 | 0.9722 | 0.8190 | |
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| 0.8398 | 15.0 | 900 | 0.5121 | 0.8237 | 0.9721 | 0.8101 | |
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| 0.6746 | 16.0 | 960 | 0.4823 | 0.8322 | 0.9742 | 0.8185 | |
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| 0.779 | 17.0 | 1020 | 0.5121 | 0.8193 | 0.9749 | 0.8002 | |
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| 0.7436 | 18.0 | 1080 | 0.4911 | 0.8270 | 0.9740 | 0.8153 | |
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| 0.7586 | 19.0 | 1140 | 0.4376 | 0.8439 | 0.9769 | 0.8346 | |
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| 0.688 | 20.0 | 1200 | 0.4732 | 0.8377 | 0.9784 | 0.8247 | |
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| 0.8294 | 21.0 | 1260 | 0.4889 | 0.8323 | 0.9777 | 0.8138 | |
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| 0.7451 | 22.0 | 1320 | 0.4015 | 0.8605 | 0.9787 | 0.8545 | |
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| 0.708 | 23.0 | 1380 | 0.3818 | 0.8677 | 0.9800 | 0.8612 | |
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| 0.6112 | 24.0 | 1440 | 0.4229 | 0.8651 | 0.9781 | 0.8565 | |
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| 0.6936 | 25.0 | 1500 | 0.3678 | 0.8714 | 0.9809 | 0.8670 | |
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| 0.6486 | 26.0 | 1560 | 0.3565 | 0.8722 | 0.9807 | 0.8685 | |
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| 0.6078 | 27.0 | 1620 | 0.3489 | 0.8760 | 0.9815 | 0.8722 | |
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| 0.6513 | 28.0 | 1680 | 0.3546 | 0.8774 | 0.9821 | 0.8727 | |
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| 0.6562 | 29.0 | 1740 | 0.3420 | 0.8816 | 0.9817 | 0.8774 | |
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| 0.6561 | 30.0 | 1800 | 0.3649 | 0.8745 | 0.9818 | 0.8691 | |
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| 0.6056 | 31.0 | 1860 | 0.3692 | 0.8730 | 0.9823 | 0.8670 | |
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| 0.5927 | 32.0 | 1920 | 0.3584 | 0.8825 | 0.9822 | 0.8779 | |
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| 0.6021 | 33.0 | 1980 | 0.3345 | 0.8870 | 0.9821 | 0.8842 | |
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| 0.6052 | 34.0 | 2040 | 0.3388 | 0.8887 | 0.9820 | 0.8868 | |
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| 0.6026 | 35.0 | 2100 | 0.3161 | 0.8938 | 0.9831 | 0.8925 | |
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| 0.5735 | 36.0 | 2160 | 0.3324 | 0.8952 | 0.9825 | 0.8957 | |
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| 0.6058 | 37.0 | 2220 | 0.3262 | 0.8932 | 0.9829 | 0.8905 | |
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| 0.5648 | 38.0 | 2280 | 0.3243 | 0.8931 | 0.9834 | 0.8899 | |
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| 0.6248 | 39.0 | 2340 | 0.3499 | 0.8825 | 0.9837 | 0.8769 | |
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| 0.4926 | 40.0 | 2400 | 0.3066 | 0.9045 | 0.9837 | 0.9045 | |
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| 0.5341 | 41.0 | 2460 | 0.3037 | 0.9042 | 0.9835 | 0.9061 | |
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| 0.5215 | 42.0 | 2520 | 0.3193 | 0.8968 | 0.9832 | 0.8967 | |
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| 0.5892 | 43.0 | 2580 | 0.3276 | 0.8910 | 0.9837 | 0.8884 | |
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| 0.559 | 44.0 | 2640 | 0.3129 | 0.9006 | 0.9843 | 0.8988 | |
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| 0.5306 | 45.0 | 2700 | 0.3137 | 0.9024 | 0.9839 | 0.9035 | |
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| 0.4789 | 46.0 | 2760 | 0.3128 | 0.8984 | 0.9842 | 0.8972 | |
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| 0.5518 | 47.0 | 2820 | 0.3117 | 0.9057 | 0.9838 | 0.9051 | |
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| 0.5201 | 48.0 | 2880 | 0.3110 | 0.9023 | 0.9842 | 0.9014 | |
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| 0.5698 | 49.0 | 2940 | 0.3031 | 0.9074 | 0.9841 | 0.9071 | |
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| 0.5227 | 50.0 | 3000 | 0.3343 | 0.9007 | 0.9834 | 0.8988 | |
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| 0.5416 | 51.0 | 3060 | 0.3117 | 0.9078 | 0.9837 | 0.9082 | |
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| 0.5882 | 52.0 | 3120 | 0.3132 | 0.9049 | 0.9835 | 0.9051 | |
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| 0.4286 | 53.0 | 3180 | 0.3133 | 0.9064 | 0.9837 | 0.9066 | |
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| 0.5278 | 54.0 | 3240 | 0.3050 | 0.9080 | 0.9847 | 0.9077 | |
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| 0.586 | 55.0 | 3300 | 0.3063 | 0.9065 | 0.9842 | 0.9082 | |
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| 0.4708 | 56.0 | 3360 | 0.3119 | 0.9058 | 0.9840 | 0.9056 | |
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| 0.5512 | 57.0 | 3420 | 0.3140 | 0.9023 | 0.9842 | 0.9014 | |
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| 0.5535 | 58.0 | 3480 | 0.3079 | 0.9046 | 0.9847 | 0.9045 | |
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| 0.4817 | 59.0 | 3540 | 0.3077 | 0.9061 | 0.9845 | 0.9061 | |
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| 0.5381 | 60.0 | 3600 | 0.3122 | 0.9015 | 0.9848 | 0.9009 | |
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| 0.6305 | 61.0 | 3660 | 0.3026 | 0.9100 | 0.9850 | 0.9103 | |
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| 0.4658 | 62.0 | 3720 | 0.3044 | 0.9093 | 0.9851 | 0.9092 | |
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| 0.4873 | 63.0 | 3780 | 0.3076 | 0.9074 | 0.9849 | 0.9071 | |
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| 0.5791 | 64.0 | 3840 | 0.3045 | 0.9077 | 0.9850 | 0.9077 | |
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| 0.5871 | 65.0 | 3900 | 0.3028 | 0.9076 | 0.9852 | 0.9071 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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