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# swin-tiny-patch4-window7-224-finetuned-st-mean-wsdmhar-auc

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1076
- Accuracy: 0.9704

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.448         | 1.0   | 53   | 1.2656          | 0.5530   |
| 0.7932        | 2.0   | 106  | 0.6547          | 0.7018   |
| 0.5983        | 3.0   | 159  | 0.4837          | 0.8106   |
| 0.4861        | 4.0   | 212  | 0.3822          | 0.8564   |
| 0.4762        | 5.0   | 265  | 0.3994          | 0.8140   |
| 0.4117        | 6.0   | 318  | 0.3143          | 0.8822   |
| 0.3659        | 7.0   | 371  | 0.2991          | 0.8819   |
| 0.355         | 8.0   | 424  | 0.3318          | 0.8688   |
| 0.3146        | 9.0   | 477  | 0.2399          | 0.9101   |
| 0.3243        | 10.0  | 530  | 0.2115          | 0.9260   |
| 0.3233        | 11.0  | 583  | 0.2022          | 0.9322   |
| 0.2736        | 12.0  | 636  | 0.1983          | 0.9256   |
| 0.2765        | 13.0  | 689  | 0.1739          | 0.9411   |
| 0.2191        | 14.0  | 742  | 0.1701          | 0.9421   |
| 0.2416        | 15.0  | 795  | 0.2053          | 0.9208   |
| 0.2039        | 16.0  | 848  | 0.1674          | 0.9401   |
| 0.2248        | 17.0  | 901  | 0.1700          | 0.9394   |
| 0.2331        | 18.0  | 954  | 0.1722          | 0.9439   |
| 0.1889        | 19.0  | 1007 | 0.1425          | 0.9470   |
| 0.1633        | 20.0  | 1060 | 0.1438          | 0.9494   |
| 0.174         | 21.0  | 1113 | 0.1357          | 0.9501   |
| 0.1599        | 22.0  | 1166 | 0.1346          | 0.9508   |
| 0.155         | 23.0  | 1219 | 0.1318          | 0.9518   |
| 0.1665        | 24.0  | 1272 | 0.1557          | 0.9477   |
| 0.1519        | 25.0  | 1325 | 0.1231          | 0.9583   |
| 0.143         | 26.0  | 1378 | 0.1247          | 0.9549   |
| 0.1393        | 27.0  | 1431 | 0.1615          | 0.9425   |
| 0.1518        | 28.0  | 1484 | 0.1246          | 0.9580   |
| 0.1239        | 29.0  | 1537 | 0.1178          | 0.9614   |
| 0.1297        | 30.0  | 1590 | 0.1141          | 0.9580   |
| 0.165         | 31.0  | 1643 | 0.1353          | 0.9539   |
| 0.1217        | 32.0  | 1696 | 0.1161          | 0.9621   |
| 0.1129        | 33.0  | 1749 | 0.1152          | 0.9618   |
| 0.1125        | 34.0  | 1802 | 0.1185          | 0.9621   |
| 0.1083        | 35.0  | 1855 | 0.1114          | 0.9642   |
| 0.1077        | 36.0  | 1908 | 0.1414          | 0.9590   |
| 0.0875        | 37.0  | 1961 | 0.1360          | 0.9559   |
| 0.1162        | 38.0  | 2014 | 0.1172          | 0.9618   |
| 0.0925        | 39.0  | 2067 | 0.1304          | 0.9583   |
| 0.109         | 40.0  | 2120 | 0.1172          | 0.9614   |
| 0.1178        | 41.0  | 2173 | 0.1525          | 0.9535   |
| 0.0886        | 42.0  | 2226 | 0.1616          | 0.9487   |
| 0.0983        | 43.0  | 2279 | 0.1197          | 0.9590   |
| 0.1209        | 44.0  | 2332 | 0.1183          | 0.9649   |
| 0.0957        | 45.0  | 2385 | 0.1268          | 0.9597   |
| 0.0919        | 46.0  | 2438 | 0.1143          | 0.9635   |
| 0.0831        | 47.0  | 2491 | 0.1319          | 0.9601   |
| 0.0888        | 48.0  | 2544 | 0.1040          | 0.9707   |
| 0.0761        | 49.0  | 2597 | 0.1088          | 0.9656   |
| 0.0843        | 50.0  | 2650 | 0.1046          | 0.9694   |
| 0.0615        | 51.0  | 2703 | 0.0982          | 0.9652   |
| 0.0705        | 52.0  | 2756 | 0.1136          | 0.9687   |
| 0.0775        | 53.0  | 2809 | 0.1272          | 0.9618   |
| 0.0739        | 54.0  | 2862 | 0.1185          | 0.9676   |
| 0.0758        | 55.0  | 2915 | 0.1185          | 0.9649   |
| 0.053         | 56.0  | 2968 | 0.1137          | 0.9663   |
| 0.0675        | 57.0  | 3021 | 0.1150          | 0.9656   |
| 0.0738        | 58.0  | 3074 | 0.1116          | 0.9676   |
| 0.067         | 59.0  | 3127 | 0.1092          | 0.9687   |
| 0.0689        | 60.0  | 3180 | 0.1116          | 0.9669   |
| 0.0647        | 61.0  | 3233 | 0.1107          | 0.9656   |
| 0.0707        | 62.0  | 3286 | 0.1183          | 0.9673   |
| 0.0708        | 63.0  | 3339 | 0.1283          | 0.9645   |
| 0.0675        | 64.0  | 3392 | 0.1222          | 0.9656   |
| 0.0622        | 65.0  | 3445 | 0.1259          | 0.9669   |
| 0.0541        | 66.0  | 3498 | 0.1142          | 0.9676   |
| 0.0528        | 67.0  | 3551 | 0.1103          | 0.9666   |
| 0.0641        | 68.0  | 3604 | 0.1363          | 0.9652   |
| 0.0448        | 69.0  | 3657 | 0.1448          | 0.9652   |
| 0.067         | 70.0  | 3710 | 0.1062          | 0.9687   |
| 0.0674        | 71.0  | 3763 | 0.1065          | 0.9697   |
| 0.0578        | 72.0  | 3816 | 0.1213          | 0.9669   |
| 0.0707        | 73.0  | 3869 | 0.1115          | 0.9659   |
| 0.0666        | 74.0  | 3922 | 0.1115          | 0.9707   |
| 0.0361        | 75.0  | 3975 | 0.1178          | 0.9680   |
| 0.047         | 76.0  | 4028 | 0.1167          | 0.9718   |
| 0.0769        | 77.0  | 4081 | 0.1073          | 0.9697   |
| 0.0422        | 78.0  | 4134 | 0.1116          | 0.9721   |
| 0.0411        | 79.0  | 4187 | 0.1186          | 0.9676   |
| 0.0402        | 80.0  | 4240 | 0.1048          | 0.9711   |
| 0.0504        | 81.0  | 4293 | 0.1105          | 0.9707   |
| 0.0579        | 82.0  | 4346 | 0.1007          | 0.9704   |
| 0.0514        | 83.0  | 4399 | 0.1105          | 0.9711   |
| 0.0398        | 84.0  | 4452 | 0.1130          | 0.9707   |
| 0.0477        | 85.0  | 4505 | 0.1097          | 0.9718   |
| 0.0413        | 86.0  | 4558 | 0.1091          | 0.9704   |
| 0.0538        | 87.0  | 4611 | 0.1068          | 0.9718   |
| 0.043         | 88.0  | 4664 | 0.1104          | 0.9725   |
| 0.0434        | 89.0  | 4717 | 0.1124          | 0.9707   |
| 0.0499        | 90.0  | 4770 | 0.1153          | 0.9711   |
| 0.0418        | 91.0  | 4823 | 0.1121          | 0.9700   |
| 0.0365        | 92.0  | 4876 | 0.1169          | 0.9711   |
| 0.0493        | 93.0  | 4929 | 0.1106          | 0.9690   |
| 0.0426        | 94.0  | 4982 | 0.1089          | 0.9680   |
| 0.0338        | 95.0  | 5035 | 0.1096          | 0.9711   |
| 0.0388        | 96.0  | 5088 | 0.1113          | 0.9694   |
| 0.0404        | 97.0  | 5141 | 0.1102          | 0.9707   |
| 0.0427        | 98.0  | 5194 | 0.1090          | 0.9704   |
| 0.0302        | 99.0  | 5247 | 0.1076          | 0.9697   |
| 0.0404        | 100.0 | 5300 | 0.1076          | 0.9704   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0.dev20240829+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1

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