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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: 0.9197530864197531 |
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- name: F1 |
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type: f1 |
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value: 0.6365832614322692 |
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- name: Recall |
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type: recall |
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value: 0.9931972789115646 |
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- name: Precision |
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type: precision |
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value: 0.9240506329113924 |
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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: 0.5002 |
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- Accuracy: 0.9198 |
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- F1: 0.6366 |
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- Auc: 0.5966 |
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- Recall: 0.9932 |
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- Precision: 0.9241 |
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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: 12 |
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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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| 0.7767 | 1.0 | 47 | 0.3346 | 0.9074 | 0.4757 | 0.5 | 1.0 | 0.9074 | |
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| 0.7767 | 2.0 | 94 | 0.5513 | 0.8272 | 0.5919 | 0.6204 | 0.8741 | 0.9312 | |
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| 0.7767 | 3.0 | 141 | 0.4290 | 0.9074 | 0.4757 | 0.5 | 1.0 | 0.9074 | |
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| 0.7767 | 4.0 | 188 | 0.4333 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 | |
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| 0.7767 | 5.0 | 235 | 0.5041 | 0.9074 | 0.6181 | 0.5898 | 0.9796 | 0.9231 | |
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| 0.7767 | 6.0 | 282 | 0.4848 | 0.9167 | 0.6317 | 0.5949 | 0.9898 | 0.9238 | |
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| 0.7767 | 7.0 | 329 | 0.4877 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 | |
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| 0.7767 | 8.0 | 376 | 0.4926 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 | |
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| 0.7767 | 9.0 | 423 | 0.4958 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 | |
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| 0.7767 | 10.0 | 470 | 0.4981 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 | |
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| 0.0381 | 11.0 | 517 | 0.4996 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 | |
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| 0.0381 | 12.0 | 564 | 0.5002 | 0.9198 | 0.6366 | 0.5966 | 0.9932 | 0.9241 | |
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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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