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# swin-camdeboo

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 camdeboo dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7505
- Accuracy: 0.5693

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6714        | 0.0647 | 100  | 2.1798          | 0.3780   |
| 1.3024        | 0.1294 | 200  | 2.0776          | 0.4628   |
| 1.2379        | 0.1942 | 300  | 2.1343          | 0.4699   |
| 0.921         | 0.2589 | 400  | 2.0805          | 0.4909   |
| 1.2239        | 0.3236 | 500  | 1.8752          | 0.5048   |
| 1.1477        | 0.3883 | 600  | 2.1270          | 0.4671   |
| 1.0999        | 0.4531 | 700  | 1.8343          | 0.5404   |
| 0.8614        | 0.5178 | 800  | 2.1993          | 0.4814   |
| 1.4409        | 0.5825 | 900  | 2.0452          | 0.5436   |
| 1.1298        | 0.6472 | 1000 | 2.0431          | 0.5234   |
| 0.6623        | 0.7120 | 1100 | 1.8523          | 0.5626   |
| 0.5556        | 0.7767 | 1200 | 1.9374          | 0.5745   |
| 0.6044        | 0.8414 | 1300 | 2.1097          | 0.5293   |
| 0.7965        | 0.9061 | 1400 | 1.7505          | 0.5693   |
| 0.6878        | 0.9709 | 1500 | 2.0018          | 0.5590   |
| 0.763         | 1.0356 | 1600 | 2.1517          | 0.5527   |
| 0.3674        | 1.1003 | 1700 | 2.5025          | 0.5642   |
| 0.6865        | 1.1650 | 1800 | 2.0337          | 0.5654   |
| 0.3911        | 1.2298 | 1900 | 2.1313          | 0.5852   |
| 0.6468        | 1.2945 | 2000 | 2.4708          | 0.5689   |
| 0.4501        | 1.3592 | 2100 | 2.2926          | 0.5911   |
| 0.2603        | 1.4239 | 2200 | 2.3958          | 0.6026   |
| 0.327         | 1.4887 | 2300 | 2.0308          | 0.5963   |
| 0.5728        | 1.5534 | 2400 | 2.5120          | 0.5860   |
| 0.3978        | 1.6181 | 2500 | 2.2434          | 0.6086   |
| 0.3691        | 1.6828 | 2600 | 2.2805          | 0.6208   |
| 0.443         | 1.7476 | 2700 | 2.2768          | 0.6097   |
| 0.5276        | 1.8123 | 2800 | 2.2888          | 0.6193   |
| 0.3785        | 1.8770 | 2900 | 2.3886          | 0.5983   |
| 0.5051        | 1.9417 | 3000 | 2.3341          | 0.5975   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1

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