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# swin-ena24

This model is a fine-tuned version of [microsoft/swin-base-simmim-window6-192](https://huggingface.co/microsoft/swin-base-simmim-window6-192) on the ena24 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4677
- Accuracy: 0.5146
- F1 Macro: 0.4328

## 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: 16
- eval_batch_size: 16
- 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: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 1.9888        | 0.2519 | 100  | 3.4122          | 0.1631   | 0.0893   |
| 1.6111        | 0.5038 | 200  | 2.9077          | 0.2578   | 0.1535   |
| 1.1276        | 0.7557 | 300  | 2.6504          | 0.3574   | 0.2827   |
| 1.0234        | 1.0076 | 400  | 2.5728          | 0.3906   | 0.3156   |
| 0.8909        | 1.2594 | 500  | 2.5007          | 0.4219   | 0.3388   |
| 0.8008        | 1.5113 | 600  | 2.7039          | 0.4043   | 0.3619   |
| 0.6885        | 1.7632 | 700  | 3.1090          | 0.3701   | 0.2926   |
| 0.839         | 2.0151 | 800  | 2.5845          | 0.4844   | 0.4149   |
| 0.325         | 2.2670 | 900  | 2.5143          | 0.5068   | 0.4128   |
| 0.4501        | 2.5189 | 1000 | 2.7684          | 0.4482   | 0.4056   |
| 0.3191        | 2.7708 | 1100 | 2.4677          | 0.5146   | 0.4328   |
| 0.1664        | 3.0227 | 1200 | 2.4777          | 0.5361   | 0.4597   |
| 0.1469        | 3.2746 | 1300 | 2.6403          | 0.5205   | 0.4495   |
| 0.3063        | 3.5264 | 1400 | 2.8000          | 0.5010   | 0.4415   |
| 0.1786        | 3.7783 | 1500 | 2.8165          | 0.5332   | 0.4525   |
| 0.0687        | 4.0302 | 1600 | 2.9027          | 0.5684   | 0.4942   |
| 0.0427        | 4.2821 | 1700 | 3.3216          | 0.4912   | 0.4362   |
| 0.1825        | 4.5340 | 1800 | 3.1456          | 0.5312   | 0.4664   |
| 0.0758        | 4.7859 | 1900 | 3.2782          | 0.5547   | 0.4578   |
| 0.0471        | 5.0378 | 2000 | 3.3348          | 0.5518   | 0.4725   |
| 0.0512        | 5.2897 | 2100 | 3.7182          | 0.5283   | 0.4514   |
| 0.0095        | 5.5416 | 2200 | 3.9028          | 0.5342   | 0.4785   |
| 0.0247        | 5.7935 | 2300 | 3.9606          | 0.5713   | 0.4879   |
| 0.0008        | 6.0453 | 2400 | 4.1290          | 0.5654   | 0.4918   |
| 0.0024        | 6.2972 | 2500 | 4.4147          | 0.5654   | 0.4863   |
| 0.0002        | 6.5491 | 2600 | 4.5209          | 0.5654   | 0.4913   |
| 0.0055        | 6.8010 | 2700 | 4.5154          | 0.5820   | 0.5067   |


### Framework versions

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

american black bear american crow eastern fox squirrel eastern gray squirrel grey fox horse northern raccoon red fox striped skunk virginia opossum white_tailed_deer wild turkey bird woodchuck bobcat chicken coyote dog domestic cat eastern chipmunk eastern cottontail