---
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: 0.9584282460136674
    - name: Precision
      type: precision
      value: 0.9575941658443274
---

<!-- 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: 0.1136
- Accuracy: 0.9584
- F1 Score: 0.9562
- Precision: 0.9576

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.2801        | 0.98  | 13   | 0.6953          | 0.7335   | 0.6819   | 0.7815    |
| 0.5928        | 1.96  | 26   | 0.3691          | 0.8440   | 0.8218   | 0.8629    |
| 0.3122        | 2.94  | 39   | 0.1664          | 0.9402   | 0.9377   | 0.9373    |
| 0.1513        | 4.0   | 53   | 0.1292          | 0.9493   | 0.9468   | 0.9467    |
| 0.1227        | 4.98  | 66   | 0.1030          | 0.9601   | 0.9577   | 0.9585    |
| 0.1201        | 5.96  | 79   | 0.1312          | 0.9522   | 0.9496   | 0.9508    |
| 0.0806        | 6.94  | 92   | 0.1306          | 0.9522   | 0.9494   | 0.9520    |
| 0.0645        | 8.0   | 106  | 0.1474          | 0.9482   | 0.9457   | 0.9490    |
| 0.0668        | 8.98  | 119  | 0.0947          | 0.9613   | 0.9589   | 0.9600    |
| 0.0577        | 9.81  | 130  | 0.1136          | 0.9584   | 0.9562   | 0.9576    |


### Framework versions

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