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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: 0.9765258215962441 |
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- name: F1 |
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type: f1 |
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value: 0.9294374875770225 |
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- name: Recall |
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type: recall |
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value: 1.0 |
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- name: Precision |
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type: precision |
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value: 0.9744897959183674 |
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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: 0.1064 |
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- Accuracy: 0.9765 |
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- F1: 0.9294 |
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- Auc: 0.8864 |
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- Recall: 1.0 |
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- Precision: 0.9745 |
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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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| 0.6804 | 1.0 | 54 | 0.3821 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 | |
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| 0.6804 | 2.0 | 108 | 0.4134 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 | |
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| 0.6804 | 3.0 | 162 | 0.2708 | 0.9061 | 0.5585 | 0.5455 | 1.0 | 0.9052 | |
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| 0.6804 | 4.0 | 216 | 0.2405 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 | |
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| 0.6804 | 5.0 | 270 | 0.2193 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 | |
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| 0.6804 | 6.0 | 324 | 0.1719 | 0.9484 | 0.8775 | 0.9310 | 0.9529 | 0.9891 | |
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| 0.6804 | 7.0 | 378 | 0.0525 | 0.9859 | 0.9612 | 0.9519 | 0.9948 | 0.9896 | |
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| 0.6804 | 8.0 | 432 | 0.0482 | 0.9906 | 0.9736 | 0.9545 | 1.0 | 0.9896 | |
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| 0.6804 | 9.0 | 486 | 0.0907 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 | |
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| 0.1258 | 10.0 | 540 | 0.1009 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 | |
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| 0.1258 | 11.0 | 594 | 0.1051 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 | |
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| 0.1258 | 12.0 | 648 | 0.1064 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 | |
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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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