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# swin-camdeboo-loc

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7931
- Accuracy: 0.6781

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.706         | 0.0633 | 100  | 1.7448          | 0.4844   |
| 1.3052        | 0.1267 | 200  | 1.9530          | 0.4844   |
| 1.1569        | 0.1900 | 300  | 1.6721          | 0.5363   |
| 1.4451        | 0.2533 | 400  | 1.6504          | 0.5812   |
| 0.9833        | 0.3167 | 500  | 2.2411          | 0.4998   |
| 1.1427        | 0.3800 | 600  | 1.5587          | 0.5720   |
| 1.0228        | 0.4433 | 700  | 1.9815          | 0.5817   |
| 0.8635        | 0.5066 | 800  | 1.6364          | 0.5953   |
| 0.8243        | 0.5700 | 900  | 1.6178          | 0.5834   |
| 0.6203        | 0.6333 | 1000 | 1.5926          | 0.5997   |
| 0.7647        | 0.6966 | 1100 | 1.9691          | 0.5491   |
| 0.6963        | 0.7600 | 1200 | 1.7098          | 0.6055   |
| 0.7896        | 0.8233 | 1300 | 1.6605          | 0.5962   |
| 0.8549        | 0.8866 | 1400 | 1.4914          | 0.6231   |
| 0.6353        | 0.9500 | 1500 | 2.0022          | 0.5495   |
| 0.5124        | 1.0133 | 1600 | 1.8427          | 0.5914   |
| 0.297         | 1.0766 | 1700 | 1.9349          | 0.6090   |
| 0.5861        | 1.1400 | 1800 | 1.7115          | 0.6667   |
| 0.7606        | 1.2033 | 1900 | 2.1216          | 0.6200   |
| 0.1236        | 1.2666 | 2000 | 2.1265          | 0.6196   |
| 0.3057        | 1.3300 | 2100 | 1.7538          | 0.6332   |
| 0.3497        | 1.3933 | 2200 | 2.1055          | 0.6072   |
| 0.5086        | 1.4566 | 2300 | 2.0850          | 0.6433   |
| 0.3447        | 1.5199 | 2400 | 1.8278          | 0.6583   |
| 0.6164        | 1.5833 | 2500 | 1.9431          | 0.6160   |
| 0.3085        | 1.6466 | 2600 | 1.5871          | 0.6874   |
| 0.5517        | 1.7099 | 2700 | 1.7441          | 0.6535   |
| 0.3404        | 1.7733 | 2800 | 1.6698          | 0.6742   |
| 0.1559        | 1.8366 | 2900 | 1.8379          | 0.6706   |
| 0.3857        | 1.8999 | 3000 | 1.8784          | 0.6583   |
| 0.2708        | 1.9633 | 3100 | 1.7931          | 0.6781   |


### Framework versions

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

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