---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_11
  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: 0.9799685781618225
    - name: Precision
      type: precision
      value: 0.9796898193485958
---

<!-- 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-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_11

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: 0.0783
- Accuracy: 0.9800
- F1 Score: 0.9796
- Precision: 0.9797
- Sensitivity: 0.9796
- Specificity: 0.9950

## 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: 0.0001
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.2367        | 0.99  | 19   | 0.3560          | 0.8649   | 0.8629   | 0.8649    | 0.8629      | 0.9648      |
| 0.2911        | 1.97  | 38   | 0.2087          | 0.9297   | 0.9290   | 0.9335    | 0.9277      | 0.9822      |
| 0.1681        | 2.96  | 57   | 0.1393          | 0.9564   | 0.9558   | 0.9558    | 0.9562      | 0.9890      |
| 0.0923        | 4.0   | 77   | 0.1106          | 0.9643   | 0.9639   | 0.9637    | 0.9647      | 0.9910      |
| 0.0602        | 4.99  | 96   | 0.1510          | 0.9505   | 0.9494   | 0.9512    | 0.9504      | 0.9875      |
| 0.0388        | 5.97  | 115  | 0.1145          | 0.9666   | 0.9667   | 0.9670    | 0.9672      | 0.9916      |
| 0.0197        | 6.96  | 134  | 0.0783          | 0.9800   | 0.9796   | 0.9797    | 0.9796      | 0.9950      |
| 0.0172        | 8.0   | 154  | 0.1032          | 0.9713   | 0.9713   | 0.9715    | 0.9718      | 0.9928      |
| 0.0169        | 8.99  | 173  | 0.0854          | 0.9776   | 0.9772   | 0.9771    | 0.9774      | 0.9944      |
| 0.0104        | 9.87  | 190  | 0.0948          | 0.9753   | 0.9750   | 0.9749    | 0.9753      | 0.9938      |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3