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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 |
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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: 0.9265375854214123 |
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- name: Precision |
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type: precision |
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value: 0.9269521372101541 |
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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 |
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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: 0.1925 |
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- Accuracy: 0.9265 |
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- F1 Score: 0.9252 |
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- Precision: 0.9270 |
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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: 1e-05 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:| |
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| 1.2212 | 0.96 | 20 | 1.1407 | 0.6429 | 0.6225 | 0.6601 | |
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| 0.565 | 1.98 | 41 | 0.5162 | 0.8326 | 0.8311 | 0.8428 | |
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| 0.3245 | 2.99 | 62 | 0.3265 | 0.8804 | 0.8784 | 0.8843 | |
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| 0.2618 | 4.0 | 83 | 0.2713 | 0.9066 | 0.9054 | 0.9105 | |
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| 0.2164 | 4.96 | 103 | 0.2812 | 0.8946 | 0.8929 | 0.8994 | |
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| 0.1814 | 5.98 | 124 | 0.2411 | 0.9060 | 0.9043 | 0.9091 | |
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| 0.1481 | 6.99 | 145 | 0.2345 | 0.9100 | 0.9084 | 0.9130 | |
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| 0.1468 | 8.0 | 166 | 0.2340 | 0.9072 | 0.9055 | 0.9108 | |
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| 0.1336 | 8.96 | 186 | 0.1925 | 0.9265 | 0.9252 | 0.9270 | |
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| 0.133 | 9.64 | 200 | 0.2021 | 0.9220 | 0.9207 | 0.9235 | |
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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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