| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - f1 |
| - recall |
| - precision |
| model-index: |
| - name: Brain_Tumor_Classification_using_swin |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9960906958561376 |
| - name: F1 |
| type: f1 |
| value: 0.9960906958561376 |
| - name: Recall |
| type: recall |
| value: 0.9960906958561376 |
| - name: Precision |
| type: precision |
| value: 0.9960906958561376 |
| --- |
| |
| <!-- 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. --> |
|
|
| # Brain_Tumor_Classification_using_swin |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0123 |
| - Accuracy: 0.9961 |
| - F1: 0.9961 |
| - Recall: 0.9961 |
| - Precision: 0.9961 |
|
|
| ## 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: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
| | 0.1234 | 1.0 | 180 | 0.0450 | 0.9840 | 0.9840 | 0.9840 | 0.9840 | |
| | 0.0837 | 2.0 | 360 | 0.0198 | 0.9926 | 0.9926 | 0.9926 | 0.9926 | |
| | 0.0373 | 3.0 | 540 | 0.0123 | 0.9961 | 0.9961 | 0.9961 | 0.9961 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.23.1 |
| - Pytorch 1.13.0 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
|
|
