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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV68

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2868
- Accuracy: 0.9314

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.0651        | 1.0     | 18   | 1.0769          | 0.4686   |
| 0.9503        | 2.0     | 36   | 0.8111          | 0.7257   |
| 0.5745        | 3.0     | 54   | 0.4972          | 0.7314   |
| 0.4746        | 4.0     | 72   | 0.4788          | 0.7486   |
| 0.4363        | 5.0     | 90   | 0.5427          | 0.7314   |
| 0.4362        | 6.0     | 108  | 0.3581          | 0.8686   |
| 0.3476        | 7.0     | 126  | 0.3572          | 0.8686   |
| 0.3113        | 8.0     | 144  | 0.4335          | 0.7886   |
| 0.3943        | 9.0     | 162  | 0.2782          | 0.8686   |
| 0.2574        | 10.0    | 180  | 0.3320          | 0.8686   |
| 0.2345        | 11.0    | 198  | 0.4383          | 0.8343   |
| 0.3002        | 12.0    | 216  | 0.3053          | 0.8686   |
| 0.2038        | 13.0    | 234  | 0.3189          | 0.8743   |
| 0.2244        | 14.0    | 252  | 0.2766          | 0.8743   |
| 0.2277        | 15.0    | 270  | 0.2637          | 0.8857   |
| 0.2318        | 16.0    | 288  | 0.4612          | 0.8114   |
| 0.1908        | 17.0    | 306  | 0.3167          | 0.8857   |
| 0.1932        | 18.0    | 324  | 0.2949          | 0.9029   |
| 0.1676        | 19.0    | 342  | 0.2627          | 0.9086   |
| 0.1442        | 20.0    | 360  | 0.2584          | 0.9143   |
| 0.1606        | 21.0    | 378  | 0.2626          | 0.9143   |
| 0.1624        | 22.0    | 396  | 0.2351          | 0.9257   |
| 0.1735        | 23.0    | 414  | 0.2746          | 0.9257   |
| 0.1604        | 24.0    | 432  | 0.3237          | 0.8914   |
| 0.122         | 25.0    | 450  | 0.2852          | 0.8914   |
| 0.1447        | 26.0    | 468  | 0.2594          | 0.92     |
| 0.1265        | 27.0    | 486  | 0.2857          | 0.9029   |
| 0.1265        | 28.0    | 504  | 0.3238          | 0.8743   |
| 0.122         | 29.0    | 522  | 0.3029          | 0.8857   |
| 0.0929        | 30.0    | 540  | 0.2936          | 0.9029   |
| 0.1276        | 31.0    | 558  | 0.2777          | 0.9143   |
| 0.1118        | 32.0    | 576  | 0.2812          | 0.9143   |
| 0.1058        | 33.0    | 594  | 0.2925          | 0.92     |
| 0.0824        | 34.0    | 612  | 0.3519          | 0.8914   |
| 0.1084        | 35.0    | 630  | 0.2847          | 0.92     |
| 0.1074        | 36.0    | 648  | 0.2735          | 0.9143   |
| 0.1415        | 37.0    | 666  | 0.2724          | 0.9257   |
| 0.0702        | 38.0    | 684  | 0.2873          | 0.92     |
| 0.0987        | 39.0    | 702  | 0.2924          | 0.92     |
| 0.0637        | 40.0    | 720  | 0.2868          | 0.9314   |
| 0.1183        | 41.0    | 738  | 0.2892          | 0.92     |
| 0.096         | 42.0    | 756  | 0.2910          | 0.9143   |
| 0.0719        | 42.5217 | 765  | 0.2897          | 0.9143   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1

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