| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: microsoft/swin-tiny-patch4-window7-224 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: swin-brain-tumor-type-classification |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # swin-brain-tumor-type-classification |
|
|
| This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2978 |
| - Accuracy: 0.9081 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 128 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 25 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 2.4824 | 1.0 | 21 | 2.2558 | 0.2420 | |
| | 2.1213 | 2.0 | 42 | 1.8165 | 0.4170 | |
| | 1.6613 | 3.0 | 63 | 1.3678 | 0.5671 | |
| | 1.3237 | 4.0 | 84 | 1.1940 | 0.6060 | |
| | 1.1543 | 5.0 | 105 | 0.9205 | 0.7049 | |
| | 0.9317 | 6.0 | 126 | 0.8121 | 0.7314 | |
| | 0.7891 | 7.0 | 147 | 0.6553 | 0.7986 | |
| | 0.6812 | 8.0 | 168 | 0.5720 | 0.8180 | |
| | 0.6348 | 9.0 | 189 | 0.5364 | 0.8180 | |
| | 0.5488 | 10.0 | 210 | 0.4780 | 0.8428 | |
| | 0.505 | 11.0 | 231 | 0.4540 | 0.8569 | |
| | 0.4758 | 12.0 | 252 | 0.3992 | 0.8852 | |
| | 0.4306 | 13.0 | 273 | 0.4280 | 0.8675 | |
| | 0.3952 | 14.0 | 294 | 0.4019 | 0.8781 | |
| | 0.3726 | 15.0 | 315 | 0.3794 | 0.8763 | |
| | 0.3191 | 16.0 | 336 | 0.3482 | 0.8958 | |
| | 0.3014 | 17.0 | 357 | 0.3372 | 0.8940 | |
| | 0.2785 | 18.0 | 378 | 0.3472 | 0.8993 | |
| | 0.2948 | 19.0 | 399 | 0.3246 | 0.9064 | |
| | 0.2618 | 20.0 | 420 | 0.3060 | 0.9081 | |
| | 0.2705 | 21.0 | 441 | 0.3122 | 0.9046 | |
| | 0.2479 | 22.0 | 462 | 0.3061 | 0.9028 | |
| | 0.2411 | 23.0 | 483 | 0.3040 | 0.9099 | |
| | 0.2556 | 24.0 | 504 | 0.2990 | 0.9099 | |
| | 0.2413 | 25.0 | 525 | 0.2978 | 0.9081 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.45.1 |
| - Pytorch 2.4.0 |
| - Datasets 3.0.1 |
| - Tokenizers 0.20.0 |
|
|
