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# swinv2-tiny-patch4-window8-256-dmae-humeda-2

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: 0.7928
- Accuracy: 0.7115

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 1.3469          | 0.5      |
| No log        | 2.0   | 4    | 1.3200          | 0.4808   |
| No log        | 3.0   | 6    | 1.3124          | 0.4808   |
| No log        | 4.0   | 8    | 1.2178          | 0.5      |
| 1.1551        | 5.0   | 10   | 1.0957          | 0.5769   |
| 1.1551        | 6.0   | 12   | 1.0359          | 0.5769   |
| 1.1551        | 7.0   | 14   | 1.0103          | 0.5962   |
| 1.1551        | 8.0   | 16   | 0.9382          | 0.6538   |
| 1.1551        | 9.0   | 18   | 0.8748          | 0.6346   |
| 0.9827        | 10.0  | 20   | 0.8836          | 0.6154   |
| 0.9827        | 11.0  | 22   | 0.8574          | 0.6154   |
| 0.9827        | 12.0  | 24   | 0.8494          | 0.5962   |
| 0.9827        | 13.0  | 26   | 0.8226          | 0.6154   |
| 0.9827        | 14.0  | 28   | 0.8242          | 0.6346   |
| 0.8007        | 15.0  | 30   | 0.8304          | 0.6154   |
| 0.8007        | 16.0  | 32   | 0.8447          | 0.6538   |
| 0.8007        | 17.0  | 34   | 0.8228          | 0.6923   |
| 0.8007        | 18.0  | 36   | 0.7928          | 0.7115   |
| 0.8007        | 19.0  | 38   | 0.7822          | 0.6731   |
| 0.6882        | 20.0  | 40   | 0.7750          | 0.6538   |
| 0.6882        | 21.0  | 42   | 0.7726          | 0.6538   |
| 0.6882        | 22.0  | 44   | 0.7898          | 0.6731   |
| 0.6882        | 23.0  | 46   | 0.8021          | 0.6731   |
| 0.6882        | 24.0  | 48   | 0.7834          | 0.6923   |
| 0.6154        | 25.0  | 50   | 0.7634          | 0.6731   |
| 0.6154        | 26.0  | 52   | 0.7584          | 0.6923   |
| 0.6154        | 27.0  | 54   | 0.7773          | 0.6538   |
| 0.6154        | 28.0  | 56   | 0.7830          | 0.6538   |
| 0.6154        | 29.0  | 58   | 0.7719          | 0.6538   |
| 0.541         | 30.0  | 60   | 0.7603          | 0.6538   |
| 0.541         | 31.0  | 62   | 0.7497          | 0.6731   |
| 0.541         | 32.0  | 64   | 0.7381          | 0.7115   |
| 0.541         | 33.0  | 66   | 0.7275          | 0.6923   |
| 0.541         | 34.0  | 68   | 0.7277          | 0.6923   |
| 0.5163        | 35.0  | 70   | 0.7271          | 0.6923   |
| 0.5163        | 36.0  | 72   | 0.7274          | 0.6923   |
| 0.5163        | 37.0  | 74   | 0.7304          | 0.6923   |
| 0.5163        | 38.0  | 76   | 0.7329          | 0.6923   |
| 0.5163        | 39.0  | 78   | 0.7351          | 0.6923   |
| 0.5183        | 40.0  | 80   | 0.7356          | 0.6923   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

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