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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: 0.9651567944250871
---
<!-- 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
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