---
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_08
  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: 0.9536527886881383
    - name: Precision
      type: precision
      value: 0.9563791141223957
---

<!-- 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_08

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: 0.1422
- Accuracy: 0.9537
- F1 Score: 0.9549
- Precision: 0.9564

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.3618        | 0.99  | 19   | 0.6238          | 0.7541   | 0.7431   | 0.7821    |
| 0.3833        | 1.97  | 38   | 0.3097          | 0.8865   | 0.8884   | 0.8970    |
| 0.2011        | 2.96  | 57   | 0.2600          | 0.9053   | 0.9078   | 0.9171    |
| 0.1124        | 4.0   | 77   | 0.1793          | 0.9328   | 0.9342   | 0.9381    |
| 0.0711        | 4.99  | 96   | 0.1385          | 0.9497   | 0.9509   | 0.9522    |
| 0.0518        | 5.97  | 115  | 0.1506          | 0.9485   | 0.9501   | 0.9523    |
| 0.0393        | 6.96  | 134  | 0.1422          | 0.9537   | 0.9549   | 0.9564    |
| 0.0361        | 8.0   | 154  | 0.1545          | 0.9482   | 0.9497   | 0.9522    |
| 0.025         | 8.99  | 173  | 0.1482          | 0.9501   | 0.9516   | 0.9541    |
| 0.0204        | 9.87  | 190  | 0.1474          | 0.9513   | 0.9527   | 0.9550    |


### Framework versions

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