---
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_13
  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: 0.9783974862529458
    - name: Precision
      type: precision
      value: 0.9787264477445259
---

<!-- 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_13

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: 0.0796
- Accuracy: 0.9784
- F1 Score: 0.9786
- Precision: 0.9787
- Sensitivity: 0.9790
- Specificity: 0.9946

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.2276        | 0.99  | 19   | 0.5721          | 0.7891   | 0.7955   | 0.8401    | 0.7933      | 0.9454      |
| 0.3873        | 1.97  | 38   | 0.2399          | 0.9195   | 0.9195   | 0.9207    | 0.9224      | 0.9796      |
| 0.1287        | 2.96  | 57   | 0.2204          | 0.9230   | 0.9237   | 0.9275    | 0.9261      | 0.9806      |
| 0.0882        | 4.0   | 77   | 0.1026          | 0.9647   | 0.9649   | 0.9647    | 0.9656      | 0.9911      |
| 0.0605        | 4.99  | 96   | 0.0898          | 0.9678   | 0.9683   | 0.9686    | 0.9685      | 0.9919      |
| 0.0439        | 5.97  | 115  | 0.0853          | 0.9741   | 0.9746   | 0.9748    | 0.9747      | 0.9935      |
| 0.0275        | 6.96  | 134  | 0.0941          | 0.9721   | 0.9724   | 0.9730    | 0.9730      | 0.9930      |
| 0.0186        | 8.0   | 154  | 0.0803          | 0.9764   | 0.9767   | 0.9770    | 0.9773      | 0.9941      |
| 0.0165        | 8.99  | 173  | 0.0740          | 0.9780   | 0.9782   | 0.9782    | 0.9786      | 0.9945      |
| 0.0106        | 9.87  | 190  | 0.0796          | 0.9784   | 0.9786   | 0.9787    | 0.9790      | 0.9946      |


### Framework versions

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