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# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV62

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: 0.2430
- Accuracy: 0.92

## 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: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 1.0856          | 0.3543   |
| 1.0609        | 2.0   | 10   | 1.0192          | 0.4571   |
| 1.0609        | 3.0   | 15   | 0.9256          | 0.5314   |
| 0.9026        | 4.0   | 20   | 0.7543          | 0.7029   |
| 0.9026        | 5.0   | 25   | 0.5856          | 0.7257   |
| 0.6347        | 6.0   | 30   | 0.3937          | 0.8629   |
| 0.6347        | 7.0   | 35   | 0.3700          | 0.8743   |
| 0.4754        | 8.0   | 40   | 0.3407          | 0.8629   |
| 0.4754        | 9.0   | 45   | 0.3551          | 0.8343   |
| 0.4252        | 10.0  | 50   | 0.2908          | 0.9086   |
| 0.4252        | 11.0  | 55   | 0.2852          | 0.8857   |
| 0.3628        | 12.0  | 60   | 0.2536          | 0.9029   |
| 0.3628        | 13.0  | 65   | 0.3290          | 0.88     |
| 0.3201        | 14.0  | 70   | 0.2594          | 0.9029   |
| 0.3201        | 15.0  | 75   | 0.2589          | 0.9029   |
| 0.2689        | 16.0  | 80   | 0.2430          | 0.92     |
| 0.2689        | 17.0  | 85   | 0.2531          | 0.9029   |
| 0.2668        | 18.0  | 90   | 0.2330          | 0.9143   |
| 0.2668        | 19.0  | 95   | 0.2579          | 0.8971   |
| 0.257         | 20.0  | 100  | 0.2669          | 0.88     |
| 0.257         | 21.0  | 105  | 0.2573          | 0.9086   |
| 0.2222        | 22.0  | 110  | 0.2863          | 0.8914   |
| 0.2222        | 23.0  | 115  | 0.2636          | 0.8971   |
| 0.1986        | 24.0  | 120  | 0.2873          | 0.8914   |
| 0.1986        | 25.0  | 125  | 0.2735          | 0.9143   |
| 0.1523        | 26.0  | 130  | 0.3072          | 0.9086   |
| 0.1523        | 27.0  | 135  | 0.2790          | 0.9143   |
| 0.1837        | 28.0  | 140  | 0.2813          | 0.9086   |
| 0.1837        | 29.0  | 145  | 0.2681          | 0.92     |
| 0.1628        | 30.0  | 150  | 0.2787          | 0.92     |
| 0.1628        | 31.0  | 155  | 0.2600          | 0.8971   |
| 0.1329        | 32.0  | 160  | 0.2849          | 0.9086   |
| 0.1329        | 33.0  | 165  | 0.3202          | 0.8914   |
| 0.155         | 34.0  | 170  | 0.2847          | 0.9086   |
| 0.155         | 35.0  | 175  | 0.2864          | 0.9029   |
| 0.1132        | 36.0  | 180  | 0.3315          | 0.8914   |
| 0.1132        | 37.0  | 185  | 0.2811          | 0.9029   |
| 0.1261        | 38.0  | 190  | 0.3471          | 0.8971   |
| 0.1261        | 39.0  | 195  | 0.3317          | 0.8971   |
| 0.1209        | 40.0  | 200  | 0.3337          | 0.8971   |
| 0.1209        | 41.0  | 205  | 0.3602          | 0.8914   |
| 0.0988        | 42.0  | 210  | 0.3385          | 0.8914   |
| 0.0988        | 43.0  | 215  | 0.4189          | 0.8971   |
| 0.0974        | 44.0  | 220  | 0.3559          | 0.9086   |
| 0.0974        | 45.0  | 225  | 0.3465          | 0.8971   |
| 0.1132        | 46.0  | 230  | 0.4038          | 0.8914   |
| 0.1132        | 47.0  | 235  | 0.3557          | 0.9029   |
| 0.0982        | 48.0  | 240  | 0.3471          | 0.9086   |
| 0.0982        | 49.0  | 245  | 0.3406          | 0.9143   |
| 0.0949        | 50.0  | 250  | 0.3637          | 0.9086   |
| 0.0949        | 51.0  | 255  | 0.3595          | 0.8971   |
| 0.0774        | 52.0  | 260  | 0.3669          | 0.8971   |
| 0.0774        | 53.0  | 265  | 0.3684          | 0.9029   |
| 0.0978        | 54.0  | 270  | 0.3868          | 0.8971   |
| 0.0978        | 55.0  | 275  | 0.3875          | 0.9029   |
| 0.0958        | 56.0  | 280  | 0.3529          | 0.9029   |
| 0.0958        | 57.0  | 285  | 0.3467          | 0.9143   |
| 0.08          | 58.0  | 290  | 0.3496          | 0.9086   |
| 0.08          | 59.0  | 295  | 0.3517          | 0.9086   |
| 0.0873        | 60.0  | 300  | 0.3534          | 0.9086   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1

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