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# swinv2-base-patch4-window8-256-dmae-humeda-DAV16

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0641
- Accuracy: 0.75

## 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: 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: 42

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.8696  | 5    | 1.5391          | 0.4038   |
| No log        | 1.8696  | 10   | 1.4350          | 0.4231   |
| 6.5563        | 2.8696  | 15   | 1.3179          | 0.5385   |
| 6.5563        | 3.8696  | 20   | 1.2358          | 0.5385   |
| 4.5658        | 4.8696  | 25   | 0.9991          | 0.5769   |
| 4.5658        | 5.8696  | 30   | 0.9567          | 0.5385   |
| 4.5658        | 6.8696  | 35   | 0.8482          | 0.6154   |
| 2.7201        | 7.8696  | 40   | 1.1108          | 0.4615   |
| 2.7201        | 8.8696  | 45   | 0.7993          | 0.6923   |
| 1.9091        | 9.8696  | 50   | 0.8539          | 0.6154   |
| 1.9091        | 10.8696 | 55   | 0.8361          | 0.6731   |
| 1.6858        | 11.8696 | 60   | 0.8574          | 0.6731   |
| 1.6858        | 12.8696 | 65   | 0.9489          | 0.6346   |
| 1.6858        | 13.8696 | 70   | 0.8122          | 0.7115   |
| 1.2131        | 14.8696 | 75   | 0.8131          | 0.6538   |
| 1.2131        | 15.8696 | 80   | 0.8591          | 0.6731   |
| 0.8967        | 16.8696 | 85   | 0.9155          | 0.6538   |
| 0.8967        | 17.8696 | 90   | 0.9712          | 0.7115   |
| 0.8967        | 18.8696 | 95   | 0.9574          | 0.6731   |
| 0.8657        | 19.8696 | 100  | 1.0001          | 0.7115   |
| 0.8657        | 20.8696 | 105  | 1.1041          | 0.5962   |
| 0.6795        | 21.8696 | 110  | 1.0165          | 0.6923   |
| 0.6795        | 22.8696 | 115  | 1.0816          | 0.6538   |
| 0.5608        | 23.8696 | 120  | 1.1195          | 0.7308   |
| 0.5608        | 24.8696 | 125  | 1.0680          | 0.6923   |
| 0.5608        | 25.8696 | 130  | 1.1495          | 0.6923   |
| 0.6841        | 26.8696 | 135  | 1.0789          | 0.7115   |
| 0.6841        | 27.8696 | 140  | 1.0814          | 0.7115   |
| 0.4526        | 28.8696 | 145  | 1.0830          | 0.6923   |
| 0.4526        | 29.8696 | 150  | 1.0641          | 0.75     |
| 0.4526        | 30.8696 | 155  | 1.1337          | 0.6731   |
| 0.4067        | 31.8696 | 160  | 1.0867          | 0.6923   |
| 0.4067        | 32.8696 | 165  | 1.1103          | 0.6731   |
| 0.4003        | 33.8696 | 170  | 1.0909          | 0.6923   |
| 0.4003        | 34.8696 | 175  | 1.0950          | 0.6731   |
| 0.4415        | 35.8696 | 180  | 1.0712          | 0.7115   |
| 0.4415        | 36.8696 | 185  | 1.0569          | 0.7115   |
| 0.4415        | 37.8696 | 190  | 1.0618          | 0.6923   |
| 0.3715        | 38.8696 | 195  | 1.0770          | 0.6923   |
| 0.3715        | 39.8696 | 200  | 1.0976          | 0.6923   |
| 0.4178        | 40.8696 | 205  | 1.1072          | 0.6923   |
| 0.4178        | 41.8696 | 210  | 1.1047          | 0.6923   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0

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