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# results

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: 0.8263
- Accuracy: 0.6462

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 1.1611          | 0.5077   |
| No log        | 2.0   | 3    | 1.1465          | 0.5077   |
| No log        | 3.0   | 5    | 1.1057          | 0.5077   |
| No log        | 4.0   | 6    | 1.1134          | 0.5077   |
| No log        | 5.0   | 7    | 1.0864          | 0.5077   |
| No log        | 6.0   | 9    | 0.9838          | 0.5385   |
| 0.5404        | 7.0   | 11   | 0.9655          | 0.5538   |
| 0.5404        | 8.0   | 12   | 0.9630          | 0.5692   |
| 0.5404        | 9.0   | 13   | 0.9631          | 0.5538   |
| 0.5404        | 10.0  | 15   | 1.0177          | 0.5385   |
| 0.5404        | 11.0  | 17   | 1.0124          | 0.5538   |
| 0.5404        | 12.0  | 18   | 0.9905          | 0.5692   |
| 0.5404        | 13.0  | 19   | 0.9473          | 0.6154   |
| 0.5207        | 14.0  | 21   | 0.9549          | 0.6      |
| 0.5207        | 15.0  | 23   | 0.9348          | 0.5846   |
| 0.5207        | 16.0  | 24   | 0.9019          | 0.5846   |
| 0.5207        | 17.0  | 25   | 0.8687          | 0.5846   |
| 0.5207        | 18.0  | 27   | 0.8462          | 0.5846   |
| 0.5207        | 19.0  | 29   | 0.8418          | 0.6154   |
| 0.5146        | 20.0  | 30   | 0.8419          | 0.6      |
| 0.5146        | 21.0  | 31   | 0.8435          | 0.5692   |
| 0.5146        | 22.0  | 33   | 0.8415          | 0.5538   |
| 0.5146        | 23.0  | 35   | 0.8293          | 0.6154   |
| 0.5146        | 24.0  | 36   | 0.8254          | 0.6      |
| 0.5146        | 25.0  | 37   | 0.8219          | 0.6154   |
| 0.5146        | 26.0  | 39   | 0.8195          | 0.6462   |
| 0.4352        | 27.0  | 41   | 0.8192          | 0.6462   |
| 0.4352        | 28.0  | 42   | 0.8198          | 0.6308   |
| 0.4352        | 29.0  | 43   | 0.8230          | 0.6615   |
| 0.4352        | 30.0  | 45   | 0.8264          | 0.6462   |
| 0.4352        | 31.0  | 47   | 0.8268          | 0.6462   |
| 0.4352        | 32.0  | 48   | 0.8266          | 0.6462   |
| 0.4352        | 33.0  | 49   | 0.8263          | 0.6462   |
| 0.4724        | 33.33 | 50   | 0.8263          | 0.6462   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1

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