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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_08 |
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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: 0.9536527886881383 |
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- name: Precision |
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type: precision |
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value: 0.9563791141223957 |
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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_08 |
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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: 0.1422 |
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- Accuracy: 0.9537 |
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- F1 Score: 0.9549 |
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- Precision: 0.9564 |
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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: 5e-05 |
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- train_batch_size: 100 |
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- eval_batch_size: 100 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 400 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:| |
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| 1.3618 | 0.99 | 19 | 0.6238 | 0.7541 | 0.7431 | 0.7821 | |
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| 0.3833 | 1.97 | 38 | 0.3097 | 0.8865 | 0.8884 | 0.8970 | |
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| 0.2011 | 2.96 | 57 | 0.2600 | 0.9053 | 0.9078 | 0.9171 | |
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| 0.1124 | 4.0 | 77 | 0.1793 | 0.9328 | 0.9342 | 0.9381 | |
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| 0.0711 | 4.99 | 96 | 0.1385 | 0.9497 | 0.9509 | 0.9522 | |
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| 0.0518 | 5.97 | 115 | 0.1506 | 0.9485 | 0.9501 | 0.9523 | |
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| 0.0393 | 6.96 | 134 | 0.1422 | 0.9537 | 0.9549 | 0.9564 | |
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| 0.0361 | 8.0 | 154 | 0.1545 | 0.9482 | 0.9497 | 0.9522 | |
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| 0.025 | 8.99 | 173 | 0.1482 | 0.9501 | 0.9516 | 0.9541 | |
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| 0.0204 | 9.87 | 190 | 0.1474 | 0.9513 | 0.9527 | 0.9550 | |
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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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