---
license: apache-2.0
base_model: 100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon
  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: 0.9651567944250871
---

<!-- 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-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon

This model is a fine-tuned version of [100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification](https://huggingface.co./100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0971
- Accuracy: 0.9652

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2504        | 0.99  | 20   | 0.1401          | 0.9512   |
| 0.2051        | 1.98  | 40   | 0.1083          | 0.9652   |
| 0.1894        | 2.96  | 60   | 0.0939          | 0.9652   |
| 0.1115        | 4.0   | 81   | 0.0880          | 0.9686   |
| 0.117         | 4.94  | 100  | 0.0971          | 0.9652   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0