---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-spa_saloon_classification
  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: 0.965195246179966
---

<!-- 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

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1075
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4613        | 1.0   | 83   | 0.3357          | 0.8718   |
| 0.2908        | 2.0   | 166  | 0.1805          | 0.9312   |
| 0.2341        | 3.0   | 249  | 0.1426          | 0.9516   |
| 0.1907        | 4.0   | 332  | 0.1471          | 0.9423   |
| 0.1836        | 5.0   | 415  | 0.1177          | 0.9576   |
| 0.1669        | 6.0   | 498  | 0.1131          | 0.9669   |
| 0.152         | 7.0   | 581  | 0.1100          | 0.9660   |
| 0.1666        | 8.0   | 664  | 0.1130          | 0.9626   |
| 0.1374        | 9.0   | 747  | 0.1132          | 0.9669   |
| 0.1278        | 10.0  | 830  | 0.1075          | 0.9652   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3