---
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_05
  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: 0.9698177676537585
    - name: Precision
      type: precision
      value: 0.9677462875813125
---

<!-- 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_05

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: 0.0792
- Accuracy: 0.9698
- F1 Score: 0.9686
- Precision: 0.9677

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.3233        | 0.98  | 13   | 0.7676          | 0.7813   | 0.7713   | 0.7796    |
| 0.6659        | 1.96  | 26   | 0.2467          | 0.9146   | 0.9111   | 0.9128    |
| 0.3122        | 2.94  | 39   | 0.1592          | 0.9442   | 0.9417   | 0.9410    |
| 0.1505        | 4.0   | 53   | 0.1241          | 0.9607   | 0.9586   | 0.9600    |
| 0.1369        | 4.98  | 66   | 0.1187          | 0.9584   | 0.9568   | 0.9562    |
| 0.1014        | 5.96  | 79   | 0.1032          | 0.9630   | 0.9614   | 0.9605    |
| 0.0701        | 6.94  | 92   | 0.0938          | 0.9641   | 0.9626   | 0.9616    |
| 0.0573        | 8.0   | 106  | 0.0941          | 0.9647   | 0.9631   | 0.9623    |
| 0.0614        | 8.98  | 119  | 0.0830          | 0.9687   | 0.9674   | 0.9666    |
| 0.0534        | 9.81  | 130  | 0.0792          | 0.9698   | 0.9686   | 0.9677    |


### Framework versions

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