---
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_07
  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: 0.9094533029612756
    - name: Precision
      type: precision
      value: 0.9188664294996836
---

<!-- 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_07

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: 0.2904
- Accuracy: 0.9095
- F1 Score: 0.9095
- Precision: 0.9189

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.3335        | 0.98  | 13   | 0.9195          | 0.6281   | 0.6111   | 0.7245    |
| 0.6062        | 1.96  | 26   | 0.6114          | 0.7625   | 0.7673   | 0.8385    |
| 0.274         | 2.94  | 39   | 0.5468          | 0.7802   | 0.7772   | 0.8533    |
| 0.1211        | 4.0   | 53   | 0.3922          | 0.8417   | 0.8417   | 0.8749    |
| 0.0991        | 4.98  | 66   | 0.4734          | 0.8172   | 0.8209   | 0.8802    |
| 0.0682        | 5.96  | 79   | 0.3751          | 0.8599   | 0.8600   | 0.8882    |
| 0.0414        | 6.94  | 92   | 0.2951          | 0.8986   | 0.8995   | 0.9100    |
| 0.0264        | 8.0   | 106  | 0.3485          | 0.8844   | 0.8855   | 0.9069    |
| 0.021         | 8.98  | 119  | 0.3803          | 0.8764   | 0.8782   | 0.9031    |
| 0.0151        | 9.81  | 130  | 0.2904          | 0.9095   | 0.9095   | 0.9189    |


### Framework versions

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