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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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- accuracy |
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- precision |
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model-index: |
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- name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_12 |
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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: 0.9760408483896308 |
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- name: Precision |
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type: precision |
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value: 0.9762470546227865 |
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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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# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_12 |
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0789 |
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- Accuracy: 0.9760 |
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- F1 Score: 0.9761 |
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- Precision: 0.9762 |
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- Sensitivity: 0.9762 |
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- Specificity: 0.9940 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:| |
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| 0.3644 | 1.0 | 30 | 0.2918 | 0.8955 | 0.8974 | 0.9070 | 0.8957 | 0.9734 | |
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| 0.2177 | 2.0 | 60 | 0.2319 | 0.9152 | 0.9155 | 0.9237 | 0.9156 | 0.9786 | |
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| 0.1171 | 3.0 | 90 | 0.1654 | 0.9489 | 0.9494 | 0.9532 | 0.9492 | 0.9872 | |
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| 0.068 | 4.0 | 120 | 0.1600 | 0.9450 | 0.9451 | 0.9466 | 0.9455 | 0.9861 | |
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| 0.0499 | 5.0 | 150 | 0.0947 | 0.9654 | 0.9656 | 0.9656 | 0.9657 | 0.9913 | |
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| 0.0302 | 6.0 | 180 | 0.0882 | 0.9713 | 0.9714 | 0.9715 | 0.9715 | 0.9928 | |
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| 0.0207 | 7.0 | 210 | 0.1002 | 0.9698 | 0.9699 | 0.9708 | 0.9699 | 0.9924 | |
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| 0.0205 | 8.0 | 240 | 0.1550 | 0.9525 | 0.9521 | 0.9544 | 0.9529 | 0.9881 | |
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| 0.0163 | 9.0 | 270 | 0.0789 | 0.9760 | 0.9761 | 0.9762 | 0.9762 | 0.9940 | |
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| 0.0181 | 10.0 | 300 | 0.0923 | 0.9737 | 0.9737 | 0.9740 | 0.9738 | 0.9934 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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