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

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: 1.0154
- 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: 4e-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    | 1.6092          | 0.4038   |
| No log        | 2.0   | 4    | 1.6075          | 0.4231   |
| No log        | 3.0   | 6    | 1.6037          | 0.4038   |
| No log        | 4.0   | 8    | 1.5960          | 0.4038   |
| 1.6041        | 5.0   | 10   | 1.5820          | 0.4038   |
| 1.6041        | 6.0   | 12   | 1.5578          | 0.4038   |
| 1.6041        | 7.0   | 14   | 1.5218          | 0.4038   |
| 1.6041        | 8.0   | 16   | 1.4849          | 0.4038   |
| 1.6041        | 9.0   | 18   | 1.4459          | 0.4038   |
| 1.4962        | 10.0  | 20   | 1.4109          | 0.4038   |
| 1.4962        | 11.0  | 22   | 1.3941          | 0.4038   |
| 1.4962        | 12.0  | 24   | 1.3865          | 0.4038   |
| 1.4962        | 13.0  | 26   | 1.3754          | 0.4038   |
| 1.4962        | 14.0  | 28   | 1.3655          | 0.4038   |
| 1.3392        | 15.0  | 30   | 1.3794          | 0.4038   |
| 1.3392        | 16.0  | 32   | 1.3800          | 0.4038   |
| 1.3392        | 17.0  | 34   | 1.3404          | 0.4038   |
| 1.3392        | 18.0  | 36   | 1.3337          | 0.4038   |
| 1.3392        | 19.0  | 38   | 1.3602          | 0.4038   |
| 1.2738        | 20.0  | 40   | 1.3384          | 0.4038   |
| 1.2738        | 21.0  | 42   | 1.3248          | 0.4038   |
| 1.2738        | 22.0  | 44   | 1.2693          | 0.4038   |
| 1.2738        | 23.0  | 46   | 1.2395          | 0.4038   |
| 1.2738        | 24.0  | 48   | 1.2427          | 0.4038   |
| 1.2283        | 25.0  | 50   | 1.2885          | 0.4038   |
| 1.2283        | 26.0  | 52   | 1.2916          | 0.4038   |
| 1.2283        | 27.0  | 54   | 1.2353          | 0.4038   |
| 1.2283        | 28.0  | 56   | 1.2032          | 0.4038   |
| 1.2283        | 29.0  | 58   | 1.2100          | 0.5577   |
| 1.1804        | 30.0  | 60   | 1.2110          | 0.6154   |
| 1.1804        | 31.0  | 62   | 1.1710          | 0.6346   |
| 1.1804        | 32.0  | 64   | 1.1323          | 0.6154   |
| 1.1804        | 33.0  | 66   | 1.1083          | 0.5962   |
| 1.1804        | 34.0  | 68   | 1.0935          | 0.5962   |
| 1.0925        | 35.0  | 70   | 1.0853          | 0.6346   |
| 1.0925        | 36.0  | 72   | 1.0622          | 0.6731   |
| 1.0925        | 37.0  | 74   | 1.0154          | 0.7115   |
| 1.0925        | 38.0  | 76   | 0.9901          | 0.7115   |
| 1.0925        | 39.0  | 78   | 0.9925          | 0.6923   |
| 0.9981        | 40.0  | 80   | 0.9865          | 0.6731   |
| 0.9981        | 41.0  | 82   | 0.9540          | 0.6731   |
| 0.9981        | 42.0  | 84   | 0.9316          | 0.7115   |
| 0.9981        | 43.0  | 86   | 0.9304          | 0.7115   |
| 0.9981        | 44.0  | 88   | 0.9246          | 0.6923   |
| 0.9102        | 45.0  | 90   | 0.8785          | 0.7115   |
| 0.9102        | 46.0  | 92   | 0.8422          | 0.7115   |
| 0.9102        | 47.0  | 94   | 0.8381          | 0.7115   |
| 0.9102        | 48.0  | 96   | 0.8359          | 0.7115   |
| 0.9102        | 49.0  | 98   | 0.8444          | 0.7115   |
| 0.8496        | 50.0  | 100  | 0.8287          | 0.6731   |
| 0.8496        | 51.0  | 102  | 0.7973          | 0.6923   |
| 0.8496        | 52.0  | 104  | 0.7799          | 0.6923   |
| 0.8496        | 53.0  | 106  | 0.7780          | 0.6923   |
| 0.8496        | 54.0  | 108  | 0.7820          | 0.7115   |
| 0.7808        | 55.0  | 110  | 0.7896          | 0.7115   |
| 0.7808        | 56.0  | 112  | 0.7737          | 0.6923   |
| 0.7808        | 57.0  | 114  | 0.7631          | 0.6731   |
| 0.7808        | 58.0  | 116  | 0.7635          | 0.6538   |
| 0.7808        | 59.0  | 118  | 0.7779          | 0.6538   |
| 0.757         | 60.0  | 120  | 0.7990          | 0.6731   |
| 0.757         | 61.0  | 122  | 0.8222          | 0.6538   |
| 0.757         | 62.0  | 124  | 0.8204          | 0.6538   |
| 0.757         | 63.0  | 126  | 0.7964          | 0.6731   |
| 0.757         | 64.0  | 128  | 0.7818          | 0.6538   |
| 0.6919        | 65.0  | 130  | 0.7796          | 0.6346   |
| 0.6919        | 66.0  | 132  | 0.7831          | 0.6346   |
| 0.6919        | 67.0  | 134  | 0.7867          | 0.6346   |
| 0.6919        | 68.0  | 136  | 0.7856          | 0.6346   |
| 0.6919        | 69.0  | 138  | 0.7793          | 0.6538   |
| 0.6722        | 70.0  | 140  | 0.7736          | 0.6538   |
| 0.6722        | 71.0  | 142  | 0.7682          | 0.6538   |
| 0.6722        | 72.0  | 144  | 0.7681          | 0.6538   |
| 0.6722        | 73.0  | 146  | 0.7672          | 0.6538   |
| 0.6722        | 74.0  | 148  | 0.7655          | 0.6538   |
| 0.6642        | 75.0  | 150  | 0.7645          | 0.6538   |
| 0.6642        | 76.0  | 152  | 0.7658          | 0.6538   |
| 0.6642        | 77.0  | 154  | 0.7677          | 0.6538   |
| 0.6642        | 78.0  | 156  | 0.7683          | 0.6538   |
| 0.6642        | 79.0  | 158  | 0.7684          | 0.6538   |
| 0.6491        | 80.0  | 160  | 0.7686          | 0.6538   |


### Framework versions

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

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