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---
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
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