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# swinv2-base-patch4-window8-256-for-pre_evaluation

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4873
- Accuracy: 0.4106

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6064        | 1.0   | 16   | 1.5189          | 0.3073   |
| 1.5058        | 2.0   | 32   | 1.5056          | 0.3073   |
| 1.5176        | 3.0   | 48   | 1.5176          | 0.2961   |
| 1.4883        | 4.0   | 64   | 1.5130          | 0.3073   |
| 1.4446        | 5.0   | 80   | 1.4540          | 0.3296   |
| 1.4568        | 6.0   | 96   | 1.5154          | 0.3156   |
| 1.4106        | 7.0   | 112  | 1.4272          | 0.3883   |
| 1.3804        | 8.0   | 128  | 1.4185          | 0.3743   |
| 1.3725        | 9.0   | 144  | 1.3943          | 0.3911   |
| 1.3441        | 10.0  | 160  | 1.4510          | 0.4022   |
| 1.3335        | 11.0  | 176  | 1.4337          | 0.3827   |
| 1.3055        | 12.0  | 192  | 1.4633          | 0.3855   |
| 1.3303        | 13.0  | 208  | 1.4674          | 0.3883   |
| 1.2882        | 14.0  | 224  | 1.4388          | 0.3911   |
| 1.2362        | 15.0  | 240  | 1.4676          | 0.3855   |
| 1.2572        | 16.0  | 256  | 1.4805          | 0.3799   |
| 1.2164        | 17.0  | 272  | 1.4717          | 0.3939   |
| 1.221         | 18.0  | 288  | 1.4354          | 0.4078   |
| 1.1713        | 19.0  | 304  | 1.4836          | 0.4078   |
| 1.18          | 20.0  | 320  | 1.4873          | 0.4106   |
| 1.1349        | 21.0  | 336  | 1.4853          | 0.3855   |
| 1.1138        | 22.0  | 352  | 1.4927          | 0.3966   |
| 1.1402        | 23.0  | 368  | 1.4672          | 0.3994   |
| 1.1183        | 24.0  | 384  | 1.5033          | 0.4022   |
| 1.0834        | 25.0  | 400  | 1.5448          | 0.3855   |
| 1.0515        | 26.0  | 416  | 1.5131          | 0.3939   |
| 1.0745        | 27.0  | 432  | 1.5314          | 0.3827   |
| 1.0332        | 28.0  | 448  | 1.5474          | 0.3939   |
| 1.0679        | 29.0  | 464  | 1.5327          | 0.3855   |
| 1.0295        | 30.0  | 480  | 1.5402          | 0.3855   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

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