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# egujr001-swim2-base-model

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: 0.1969
- Accuracy: 0.9457

## 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: 64
- eval_batch_size: 64
- seed: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6254        | 0.04  | 100  | 0.5840          | 0.7050   |
| 0.3998        | 0.08  | 200  | 0.3525          | 0.8507   |
| 0.2796        | 0.12  | 300  | 0.2710          | 0.8975   |
| 0.23          | 0.15  | 400  | 0.2660          | 0.9012   |
| 0.2372        | 0.19  | 500  | 0.1678          | 0.9401   |
| 0.1944        | 0.23  | 600  | 0.1437          | 0.9437   |
| 0.1635        | 0.27  | 700  | 0.1231          | 0.9503   |
| 0.1463        | 0.31  | 800  | 0.1353          | 0.9551   |
| 0.1287        | 0.35  | 900  | 0.1216          | 0.9523   |
| 0.1208        | 0.39  | 1000 | 0.1695          | 0.9351   |
| 0.1204        | 0.42  | 1100 | 0.1221          | 0.9557   |
| 0.1064        | 0.46  | 1200 | 0.1605          | 0.9432   |
| 0.1114        | 0.5   | 1300 | 0.0998          | 0.9613   |
| 0.1324        | 0.54  | 1400 | 0.0888          | 0.9650   |
| 0.0997        | 0.58  | 1500 | 0.0810          | 0.9686   |
| 0.0904        | 0.62  | 1600 | 0.0945          | 0.9655   |
| 0.0975        | 0.66  | 1700 | 0.0978          | 0.9635   |
| 0.0859        | 0.69  | 1800 | 0.0858          | 0.9696   |
| 0.0785        | 0.73  | 1900 | 0.0749          | 0.9722   |
| 0.0743        | 0.77  | 2000 | 0.0763          | 0.9727   |
| 0.0815        | 0.81  | 2100 | 0.0765          | 0.9728   |
| 0.0674        | 0.85  | 2200 | 0.0881          | 0.9703   |
| 0.0726        | 0.89  | 2300 | 0.0875          | 0.9716   |
| 0.0633        | 0.93  | 2400 | 0.0912          | 0.9721   |
| 0.0501        | 0.96  | 2500 | 0.0743          | 0.9750   |
| 0.0927        | 1.0   | 2600 | 0.0695          | 0.9759   |
| 0.0766        | 1.04  | 2700 | 0.0788          | 0.9733   |
| 0.0934        | 1.08  | 2800 | 0.0699          | 0.9753   |
| 0.0714        | 1.12  | 2900 | 0.0756          | 0.9762   |
| 0.069         | 1.16  | 3000 | 0.0859          | 0.9706   |
| 0.0702        | 1.2   | 3100 | 0.1001          | 0.9658   |
| 0.0633        | 1.23  | 3200 | 0.0724          | 0.9756   |
| 0.0756        | 1.27  | 3300 | 0.0734          | 0.9745   |
| 0.0617        | 1.31  | 3400 | 0.0704          | 0.9747   |
| 0.0498        | 1.35  | 3500 | 0.0651          | 0.9788   |
| 0.0668        | 1.39  | 3600 | 0.0625          | 0.9791   |
| 0.0441        | 1.43  | 3700 | 0.0714          | 0.9774   |
| 0.0789        | 1.46  | 3800 | 0.0880          | 0.9722   |
| 0.0464        | 1.5   | 3900 | 0.0720          | 0.9749   |
| 0.0532        | 1.54  | 4000 | 0.0681          | 0.9782   |
| 0.0677        | 1.58  | 4100 | 0.0733          | 0.9736   |
| 0.0654        | 1.62  | 4200 | 0.0610          | 0.9802   |
| 0.0554        | 1.66  | 4300 | 0.0825          | 0.9740   |
| 0.0836        | 1.7   | 4400 | 0.0694          | 0.9780   |
| 0.0688        | 1.73  | 4500 | 0.0599          | 0.9813   |
| 0.052         | 1.77  | 4600 | 0.0932          | 0.9673   |
| 0.0515        | 1.81  | 4700 | 0.0785          | 0.9759   |
| 0.0586        | 1.85  | 4800 | 0.0660          | 0.9787   |
| 0.056         | 1.89  | 4900 | 0.0612          | 0.9783   |
| 0.037         | 1.93  | 5000 | 0.0645          | 0.9795   |
| 0.0541        | 1.97  | 5100 | 0.0600          | 0.9809   |
| 0.0521        | 2.0   | 5200 | 0.0876          | 0.9737   |
| 0.0352        | 2.04  | 5300 | 0.0709          | 0.9780   |
| 0.0498        | 2.08  | 5400 | 0.0610          | 0.9809   |
| 0.0424        | 2.12  | 5500 | 0.0569          | 0.9830   |
| 0.0532        | 2.16  | 5600 | 0.0625          | 0.9820   |
| 0.046         | 2.2   | 5700 | 0.0512          | 0.9842   |
| 0.0453        | 2.24  | 5800 | 0.0608          | 0.9813   |
| 0.0577        | 2.27  | 5900 | 0.0697          | 0.9811   |
| 0.0397        | 2.31  | 6000 | 0.0688          | 0.9816   |
| 0.0494        | 2.35  | 6100 | 0.0534          | 0.9834   |
| 0.0158        | 2.39  | 6200 | 0.0860          | 0.9774   |
| 0.0297        | 2.43  | 6300 | 0.0593          | 0.9836   |
| 0.055         | 2.47  | 6400 | 0.0579          | 0.9821   |
| 0.0368        | 2.51  | 6500 | 0.0729          | 0.9796   |
| 0.0754        | 2.54  | 6600 | 0.0601          | 0.9827   |
| 0.0523        | 2.58  | 6700 | 0.0597          | 0.9824   |
| 0.0433        | 2.62  | 6800 | 0.0547          | 0.9841   |
| 0.0164        | 2.66  | 6900 | 0.0620          | 0.9827   |
| 0.015         | 2.7   | 7000 | 0.0639          | 0.9822   |
| 0.0415        | 2.74  | 7100 | 0.0576          | 0.9837   |
| 0.0257        | 2.78  | 7200 | 0.0620          | 0.9820   |
| 0.0268        | 2.81  | 7300 | 0.0568          | 0.9837   |
| 0.043         | 2.85  | 7400 | 0.0558          | 0.9836   |
| 0.0339        | 2.89  | 7500 | 0.0554          | 0.9839   |
| 0.0263        | 2.93  | 7600 | 0.0552          | 0.9837   |
| 0.0428        | 2.97  | 7700 | 0.0535          | 0.9842   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.13.3

fake real