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vit-base-patch16-224-in21k_covid_19_ct_scans

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  1. README.md +114 -16
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8466666666666667
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  - name: F1
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  type: f1
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- value: 0.8571428571428571
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  - name: Recall
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  type: recall
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- value: 0.8625
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  - name: Precision
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  type: precision
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- value: 0.8518518518518519
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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
@@ -44,11 +44,12 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.3062
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- - Accuracy: 0.8467
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- - F1: 0.8571
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- - Recall: 0.8625
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- - Precision: 0.8519
 
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  ## Model description
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@@ -68,20 +69,117 @@ More information needed
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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: 32
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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: 3
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
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- | 0.6963 | 1.0 | 19 | 0.5246 | 0.76 | 0.7857 | 0.825 | 0.75 |
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- | 0.6963 | 2.0 | 38 | 0.3911 | 0.8333 | 0.8322 | 0.775 | 0.8986 |
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- | 0.6963 | 3.0 | 57 | 0.3062 | 0.8467 | 0.8571 | 0.8625 | 0.8519 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.8887841658812441
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  - name: F1
30
  type: f1
31
+ 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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45
  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.
46
  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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  The following hyperparameters were used during training:
71
  - learning_rate: 0.0002
72
+ - train_batch_size: 16
73
  - eval_batch_size: 16
74
  - seed: 42
75
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
76
  - 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 | 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 | 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 | 97.0 | 25802 | 1.7232 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 |
180
+ | 0.0 | 98.0 | 26068 | 1.7260 | 0.8888 | 0.7573 | 0.7179 | 0.9729 | 0.9017 |
181
+ | 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
model.safetensors CHANGED
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