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# swinv2-tiny-patch4-window8-256-ve-UH2

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: 0.7448
- Accuracy: 0.7308

## 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 1.6091          | 0.4038   |
| No log        | 2.0   | 4    | 1.6070          | 0.4231   |
| No log        | 3.0   | 6    | 1.6017          | 0.4038   |
| No log        | 4.0   | 8    | 1.5911          | 0.4038   |
| 1.6022        | 5.0   | 10   | 1.5707          | 0.4038   |
| 1.6022        | 6.0   | 12   | 1.5355          | 0.4038   |
| 1.6022        | 7.0   | 14   | 1.4951          | 0.4038   |
| 1.6022        | 8.0   | 16   | 1.4528          | 0.4038   |
| 1.6022        | 9.0   | 18   | 1.4096          | 0.4038   |
| 1.4645        | 10.0  | 20   | 1.3820          | 0.4038   |
| 1.4645        | 11.0  | 22   | 1.4055          | 0.4038   |
| 1.4645        | 12.0  | 24   | 1.3765          | 0.4038   |
| 1.4645        | 13.0  | 26   | 1.3820          | 0.4038   |
| 1.4645        | 14.0  | 28   | 1.3712          | 0.4038   |
| 1.3172        | 15.0  | 30   | 1.3546          | 0.4038   |
| 1.3172        | 16.0  | 32   | 1.3637          | 0.4038   |
| 1.3172        | 17.0  | 34   | 1.3646          | 0.4038   |
| 1.3172        | 18.0  | 36   | 1.3271          | 0.4038   |
| 1.3172        | 19.0  | 38   | 1.3084          | 0.4038   |
| 1.2549        | 20.0  | 40   | 1.3402          | 0.4038   |
| 1.2549        | 21.0  | 42   | 1.3550          | 0.4038   |
| 1.2549        | 22.0  | 44   | 1.2677          | 0.4038   |
| 1.2549        | 23.0  | 46   | 1.2093          | 0.4038   |
| 1.2549        | 24.0  | 48   | 1.2040          | 0.4231   |
| 1.2092        | 25.0  | 50   | 1.2963          | 0.4231   |
| 1.2092        | 26.0  | 52   | 1.2917          | 0.4808   |
| 1.2092        | 27.0  | 54   | 1.1798          | 0.5769   |
| 1.2092        | 28.0  | 56   | 1.1047          | 0.6346   |
| 1.2092        | 29.0  | 58   | 1.0923          | 0.6731   |
| 1.1321        | 30.0  | 60   | 1.1066          | 0.6538   |
| 1.1321        | 31.0  | 62   | 1.0874          | 0.6538   |
| 1.1321        | 32.0  | 64   | 1.0548          | 0.6731   |
| 1.1321        | 33.0  | 66   | 1.0012          | 0.6538   |
| 1.1321        | 34.0  | 68   | 0.9641          | 0.6923   |
| 1.0022        | 35.0  | 70   | 0.9796          | 0.6538   |
| 1.0022        | 36.0  | 72   | 0.9631          | 0.6538   |
| 1.0022        | 37.0  | 74   | 0.9040          | 0.6731   |
| 1.0022        | 38.0  | 76   | 0.8731          | 0.6923   |
| 1.0022        | 39.0  | 78   | 0.8960          | 0.6731   |
| 0.8941        | 40.0  | 80   | 0.9133          | 0.6538   |
| 0.8941        | 41.0  | 82   | 0.8507          | 0.6923   |
| 0.8941        | 42.0  | 84   | 0.8064          | 0.7115   |
| 0.8941        | 43.0  | 86   | 0.8075          | 0.7115   |
| 0.8941        | 44.0  | 88   | 0.8486          | 0.6923   |
| 0.7866        | 45.0  | 90   | 0.8075          | 0.6923   |
| 0.7866        | 46.0  | 92   | 0.7496          | 0.6731   |
| 0.7866        | 47.0  | 94   | 0.7431          | 0.6731   |
| 0.7866        | 48.0  | 96   | 0.7442          | 0.6731   |
| 0.7866        | 49.0  | 98   | 0.7735          | 0.6923   |
| 0.7281        | 50.0  | 100  | 0.7751          | 0.6923   |
| 0.7281        | 51.0  | 102  | 0.7370          | 0.6923   |
| 0.7281        | 52.0  | 104  | 0.7230          | 0.6923   |
| 0.7281        | 53.0  | 106  | 0.7314          | 0.6923   |
| 0.7281        | 54.0  | 108  | 0.7498          | 0.6731   |
| 0.6725        | 55.0  | 110  | 0.7557          | 0.6731   |
| 0.6725        | 56.0  | 112  | 0.7314          | 0.7115   |
| 0.6725        | 57.0  | 114  | 0.7334          | 0.7115   |
| 0.6725        | 58.0  | 116  | 0.7375          | 0.7115   |
| 0.6725        | 59.0  | 118  | 0.7434          | 0.6923   |
| 0.6526        | 60.0  | 120  | 0.7548          | 0.6731   |
| 0.6526        | 61.0  | 122  | 0.7813          | 0.7115   |
| 0.6526        | 62.0  | 124  | 0.7722          | 0.6923   |
| 0.6526        | 63.0  | 126  | 0.7469          | 0.6923   |
| 0.6526        | 64.0  | 128  | 0.7402          | 0.6731   |
| 0.5915        | 65.0  | 130  | 0.7448          | 0.7308   |
| 0.5915        | 66.0  | 132  | 0.7467          | 0.6923   |
| 0.5915        | 67.0  | 134  | 0.7496          | 0.6731   |
| 0.5915        | 68.0  | 136  | 0.7518          | 0.7308   |
| 0.5915        | 69.0  | 138  | 0.7453          | 0.7115   |
| 0.578         | 70.0  | 140  | 0.7385          | 0.6923   |
| 0.578         | 71.0  | 142  | 0.7411          | 0.6731   |
| 0.578         | 72.0  | 144  | 0.7442          | 0.6731   |
| 0.578         | 73.0  | 146  | 0.7440          | 0.6731   |
| 0.578         | 74.0  | 148  | 0.7428          | 0.6923   |
| 0.5826        | 75.0  | 150  | 0.7414          | 0.6923   |
| 0.5826        | 76.0  | 152  | 0.7416          | 0.6923   |
| 0.5826        | 77.0  | 154  | 0.7414          | 0.6923   |
| 0.5826        | 78.0  | 156  | 0.7413          | 0.6731   |
| 0.5826        | 79.0  | 158  | 0.7413          | 0.6731   |
| 0.5586        | 80.0  | 160  | 0.7415          | 0.6923   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0

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