---
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_06
  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: 0.9225512528473804
    - name: Precision
      type: precision
      value: 0.9214370287637926
---

<!-- 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_06

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: 0.2384
- Accuracy: 0.9226
- F1 Score: 0.9210
- Precision: 0.9214

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.3082        | 0.98  | 13   | 0.7669          | 0.7819   | 0.7689   | 0.7723    |
| 0.5415        | 1.96  | 26   | 0.3100          | 0.8867   | 0.8812   | 0.8816    |
| 0.279         | 2.94  | 39   | 0.2901          | 0.8992   | 0.8967   | 0.8961    |
| 0.1563        | 4.0   | 53   | 0.2655          | 0.9089   | 0.9078   | 0.9084    |
| 0.1304        | 4.98  | 66   | 0.2971          | 0.8964   | 0.8935   | 0.8958    |
| 0.1058        | 5.96  | 79   | 0.2358          | 0.9243   | 0.9218   | 0.9224    |
| 0.0971        | 6.94  | 92   | 0.2298          | 0.9260   | 0.9245   | 0.9258    |
| 0.079         | 8.0   | 106  | 0.2468          | 0.9134   | 0.9123   | 0.9125    |
| 0.0638        | 8.98  | 119  | 0.2534          | 0.9112   | 0.9097   | 0.9101    |
| 0.0538        | 9.81  | 130  | 0.2384          | 0.9226   | 0.9210   | 0.9214    |


### Framework versions

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