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---
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: 0.9930313588850174
---
<!-- 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: 0.0408
- Accuracy: 0.9930
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2637 | 0.99 | 20 | 0.1274 | 0.9443 |
| 0.2582 | 1.98 | 40 | 0.0937 | 0.9756 |
| 0.161 | 2.96 | 60 | 0.0924 | 0.9582 |
| 0.1535 | 4.0 | 81 | 0.0612 | 0.9861 |
| 0.1347 | 4.99 | 101 | 0.0536 | 0.9791 |
| 0.1155 | 5.98 | 121 | 0.0408 | 0.9930 |
| 0.1306 | 6.96 | 141 | 0.0417 | 0.9930 |
| 0.1017 | 8.0 | 162 | 0.0380 | 0.9895 |
| 0.0859 | 8.99 | 182 | 0.0417 | 0.9895 |
| 0.0897 | 9.88 | 200 | 0.0393 | 0.9895 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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