---
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
  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: 0.9219817767653758
    - name: Precision
      type: precision
      value: 0.9235132299409764
---

<!-- 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

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: 0.2021
- Accuracy: 0.9220
- F1 Score: 0.9207
- Precision: 0.9235

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.2212        | 0.96  | 20   | 1.1407          | 0.6429   | 0.6225   | 0.6601    |
| 0.565         | 1.98  | 41   | 0.5162          | 0.8326   | 0.8311   | 0.8428    |
| 0.3245        | 2.99  | 62   | 0.3265          | 0.8804   | 0.8784   | 0.8843    |
| 0.2618        | 4.0   | 83   | 0.2713          | 0.9066   | 0.9054   | 0.9105    |
| 0.2164        | 4.96  | 103  | 0.2812          | 0.8946   | 0.8929   | 0.8994    |
| 0.1814        | 5.98  | 124  | 0.2411          | 0.9060   | 0.9043   | 0.9091    |
| 0.1481        | 6.99  | 145  | 0.2345          | 0.9100   | 0.9084   | 0.9130    |
| 0.1468        | 8.0   | 166  | 0.2340          | 0.9072   | 0.9055   | 0.9108    |
| 0.1336        | 8.96  | 186  | 0.1925          | 0.9265   | 0.9252   | 0.9270    |
| 0.133         | 9.64  | 200  | 0.2021          | 0.9220   | 0.9207   | 0.9235    |


### Framework versions

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