---
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: 0.9591516103692066
    - name: Precision
      type: precision
      value: 0.9627515459909033
---

<!-- 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: 0.1210
- Accuracy: 0.9592
- F1 Score: 0.9600
- Precision: 0.9628

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.2882        | 0.99  | 19   | 0.5469          | 0.7962   | 0.7863   | 0.8077    |
| 0.3491        | 1.97  | 38   | 0.3030          | 0.8861   | 0.8878   | 0.8981    |
| 0.1791        | 2.96  | 57   | 0.2077          | 0.9211   | 0.9229   | 0.9307    |
| 0.122         | 4.0   | 77   | 0.2007          | 0.9254   | 0.9272   | 0.9369    |
| 0.0671        | 4.99  | 96   | 0.2073          | 0.9269   | 0.9294   | 0.9401    |
| 0.0474        | 5.97  | 115  | 0.1384          | 0.9482   | 0.9494   | 0.9547    |
| 0.032         | 6.96  | 134  | 0.1683          | 0.9430   | 0.9447   | 0.9511    |
| 0.0225        | 8.0   | 154  | 0.1101          | 0.9650   | 0.9657   | 0.9671    |
| 0.0193        | 8.99  | 173  | 0.1372          | 0.9533   | 0.9544   | 0.9585    |
| 0.0193        | 9.87  | 190  | 0.1210          | 0.9592   | 0.9600   | 0.9628    |


### Framework versions

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