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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: vit-base-patch16-224-in21k_covid_19_ct_scans |
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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: Accuracy |
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type: accuracy |
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value: 0.8887841658812441 |
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- name: F1 |
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type: f1 |
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value: 0.7572553125484722 |
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- name: Recall |
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type: recall |
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value: 0.9729119638826185 |
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- name: Precision |
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type: precision |
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value: 0.9016736401673641 |
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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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# vit-base-patch16-224-in21k_covid_19_ct_scans |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7287 |
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- Accuracy: 0.8888 |
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- F1: 0.7573 |
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- Auc: 0.7179 |
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- Recall: 0.9729 |
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- Precision: 0.9017 |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Recall | Precision | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:| |
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| 0.768 | 1.0 | 266 | 0.4546 | 0.8351 | 0.4551 | 0.5 | 1.0 | 0.8351 | |
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| 0.4516 | 2.0 | 532 | 0.4498 | 0.8351 | 0.4551 | 0.5 | 1.0 | 0.8351 | |
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| 0.4516 | 3.0 | 798 | 0.4492 | 0.8351 | 0.4551 | 0.5 | 1.0 | 0.8351 | |
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| 0.4521 | 4.0 | 1064 | 0.4486 | 0.8351 | 0.4551 | 0.5 | 1.0 | 0.8351 | |
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| 0.4521 | 5.0 | 1330 | 0.4457 | 0.8351 | 0.4551 | 0.5 | 1.0 | 0.8351 | |
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| 0.4415 | 6.0 | 1596 | 0.4422 | 0.8351 | 0.4551 | 0.5 | 1.0 | 0.8351 | |
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| 0.4415 | 7.0 | 1862 | 0.4249 | 0.8351 | 0.4551 | 0.5 | 1.0 | 0.8351 | |
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| 0.4344 | 8.0 | 2128 | 0.4644 | 0.8351 | 0.4966 | 0.5183 | 0.9910 | 0.8402 | |
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| 0.4344 | 9.0 | 2394 | 0.4209 | 0.8407 | 0.5272 | 0.5355 | 0.9910 | 0.8450 | |
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| 0.3848 | 10.0 | 2660 | 0.4336 | 0.8030 | 0.6572 | 0.6642 | 0.8713 | 0.8904 | |
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| 0.3848 | 11.0 | 2926 | 0.4307 | 0.8407 | 0.6595 | 0.6387 | 0.9402 | 0.8778 | |
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| 0.2882 | 12.0 | 3192 | 0.5094 | 0.8219 | 0.6913 | 0.7007 | 0.8815 | 0.9029 | |
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| 0.2882 | 13.0 | 3458 | 0.4620 | 0.8520 | 0.6637 | 0.6363 | 0.9582 | 0.8762 | |
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| 0.1654 | 14.0 | 3724 | 0.5891 | 0.8351 | 0.7142 | 0.7247 | 0.8894 | 0.9110 | |
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| 0.1654 | 15.0 | 3990 | 0.5602 | 0.8417 | 0.6940 | 0.6828 | 0.9199 | 0.8936 | |
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| 0.0868 | 16.0 | 4256 | 0.5928 | 0.8690 | 0.7114 | 0.6785 | 0.9628 | 0.8895 | |
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| 0.045 | 17.0 | 4522 | 0.6154 | 0.8633 | 0.7268 | 0.7072 | 0.9402 | 0.9005 | |
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| 0.045 | 18.0 | 4788 | 0.6358 | 0.8680 | 0.7370 | 0.7169 | 0.9424 | 0.9037 | |
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| 0.021 | 19.0 | 5054 | 0.8247 | 0.8530 | 0.7379 | 0.7423 | 0.9074 | 0.9157 | |
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| 0.021 | 20.0 | 5320 | 0.9930 | 0.8473 | 0.7229 | 0.7229 | 0.9086 | 0.9086 | |
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| 0.0136 | 21.0 | 5586 | 0.5601 | 0.8652 | 0.7262 | 0.7038 | 0.9447 | 0.8990 | |
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| 0.0136 | 22.0 | 5852 | 0.6475 | 0.8699 | 0.6935 | 0.6562 | 0.9752 | 0.8816 | |
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| 0.0464 | 23.0 | 6118 | 0.5767 | 0.8567 | 0.7273 | 0.7170 | 0.9255 | 0.9051 | |
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| 0.0464 | 24.0 | 6384 | 0.7394 | 0.8501 | 0.7369 | 0.7452 | 0.9018 | 0.9173 | |
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| 0.0438 | 25.0 | 6650 | 0.7622 | 0.8680 | 0.6781 | 0.6413 | 0.9797 | 0.8768 | |
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| 0.0438 | 26.0 | 6916 | 0.7617 | 0.8831 | 0.7509 | 0.7168 | 0.9650 | 0.9019 | |
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| 0.0126 | 27.0 | 7182 | 0.8841 | 0.8624 | 0.7354 | 0.7227 | 0.9312 | 0.9066 | |
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| 0.0126 | 28.0 | 7448 | 0.7538 | 0.8784 | 0.7544 | 0.7300 | 0.9515 | 0.9074 | |
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| 0.016 | 29.0 | 7714 | 0.7106 | 0.8718 | 0.6709 | 0.6321 | 0.9898 | 0.8735 | |
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| 0.016 | 30.0 | 7980 | 0.6112 | 0.8756 | 0.7251 | 0.6893 | 0.9673 | 0.8927 | |
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| 0.0384 | 31.0 | 8246 | 0.5990 | 0.8784 | 0.7271 | 0.6887 | 0.9718 | 0.8922 | |
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| 0.0276 | 32.0 | 8512 | 0.6617 | 0.8850 | 0.7411 | 0.6996 | 0.9763 | 0.8954 | |
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| 0.0276 | 33.0 | 8778 | 0.7069 | 0.8907 | 0.7599 | 0.7190 | 0.9752 | 0.9019 | |
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| 0.0109 | 34.0 | 9044 | 0.8042 | 0.8746 | 0.6974 | 0.6567 | 0.9819 | 0.8815 | |
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| 0.0109 | 35.0 | 9310 | 0.7706 | 0.8831 | 0.7369 | 0.6962 | 0.9752 | 0.8944 | |
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| 0.0028 | 36.0 | 9576 | 0.8394 | 0.8869 | 0.7516 | 0.7122 | 0.9729 | 0.8998 | |
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| 0.0028 | 37.0 | 9842 | 0.8954 | 0.8850 | 0.7475 | 0.7087 | 0.9718 | 0.8987 | |
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| 0.0076 | 38.0 | 10108 | 0.9389 | 0.8850 | 0.7475 | 0.7087 | 0.9718 | 0.8987 | |
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| 0.0076 | 39.0 | 10374 | 0.9697 | 0.8850 | 0.7475 | 0.7087 | 0.9718 | 0.8987 | |
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| 0.0001 | 40.0 | 10640 | 0.9954 | 0.8850 | 0.7475 | 0.7087 | 0.9718 | 0.8987 | |
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| 0.0001 | 41.0 | 10906 | 1.0169 | 0.8850 | 0.7475 | 0.7087 | 0.9718 | 0.8987 | |
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| 0.0 | 42.0 | 11172 | 1.0381 | 0.8860 | 0.7488 | 0.7093 | 0.9729 | 0.8989 | |
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| 0.0 | 43.0 | 11438 | 1.0582 | 0.8860 | 0.7488 | 0.7093 | 0.9729 | 0.8989 | |
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| 0.0 | 44.0 | 11704 | 1.0763 | 0.8860 | 0.7488 | 0.7093 | 0.9729 | 0.8989 | |
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| 0.0 | 45.0 | 11970 | 1.0937 | 0.8860 | 0.7488 | 0.7093 | 0.9729 | 0.8989 | |
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| 0.0 | 46.0 | 12236 | 1.1095 | 0.8878 | 0.7545 | 0.7150 | 0.9729 | 0.9007 | |
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| 0.0 | 47.0 | 12502 | 1.1263 | 0.8878 | 0.7545 | 0.7150 | 0.9729 | 0.9007 | |
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| 0.0 | 48.0 | 12768 | 1.1427 | 0.8878 | 0.7545 | 0.7150 | 0.9729 | 0.9007 | |
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| 0.0 | 49.0 | 13034 | 1.1587 | 0.8878 | 0.7545 | 0.7150 | 0.9729 | 0.9007 | |
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| 0.0 | 50.0 | 13300 | 1.1745 | 0.8878 | 0.7545 | 0.7150 | 0.9729 | 0.9007 | |
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| 0.0 | 51.0 | 13566 | 1.1901 | 0.8878 | 0.7545 | 0.7150 | 0.9729 | 0.9007 | |
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| 0.0 | 52.0 | 13832 | 1.2052 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 53.0 | 14098 | 1.2201 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 54.0 | 14364 | 1.2350 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 55.0 | 14630 | 1.2497 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 56.0 | 14896 | 1.2641 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 57.0 | 15162 | 1.2785 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 58.0 | 15428 | 1.2925 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 59.0 | 15694 | 1.3068 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 60.0 | 15960 | 1.3207 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 61.0 | 16226 | 1.3346 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 62.0 | 16492 | 1.3485 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 63.0 | 16758 | 1.3622 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 64.0 | 17024 | 1.3758 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 65.0 | 17290 | 1.3893 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 66.0 | 17556 | 1.4029 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 67.0 | 17822 | 1.4166 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 68.0 | 18088 | 1.4298 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 69.0 | 18354 | 1.4431 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 70.0 | 18620 | 1.4566 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 71.0 | 18886 | 1.4695 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 72.0 | 19152 | 1.4824 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 73.0 | 19418 | 1.4950 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 74.0 | 19684 | 1.5076 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 75.0 | 19950 | 1.5201 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 76.0 | 20216 | 1.5321 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 77.0 | 20482 | 1.5441 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 78.0 | 20748 | 1.5564 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 79.0 | 21014 | 1.5691 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 80.0 | 21280 | 1.5800 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 81.0 | 21546 | 1.5910 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 82.0 | 21812 | 1.6021 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 83.0 | 22078 | 1.6133 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 84.0 | 22344 | 1.6244 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 85.0 | 22610 | 1.6357 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 86.0 | 22876 | 1.6468 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 87.0 | 23142 | 1.6580 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 88.0 | 23408 | 1.6694 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 89.0 | 23674 | 1.6806 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 90.0 | 23940 | 1.6876 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 91.0 | 24206 | 1.6938 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 92.0 | 24472 | 1.6996 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 93.0 | 24738 | 1.7051 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 94.0 | 25004 | 1.7104 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 95.0 | 25270 | 1.7152 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 96.0 | 25536 | 1.7195 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 97.0 | 25802 | 1.7232 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 98.0 | 26068 | 1.7260 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 99.0 | 26334 | 1.7280 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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| 0.0 | 100.0 | 26600 | 1.7287 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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