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# swin-tiny-patch4-window7-224-finetuned-st-wsdmhar-xyz-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: 0.1060
- Accuracy: 0.9766

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.586         | 1.0   | 53   | 1.4366          | 0.4559   |
| 0.9024        | 2.0   | 106  | 0.7526          | 0.7018   |
| 0.6157        | 3.0   | 159  | 0.5375          | 0.7944   |
| 0.5165        | 4.0   | 212  | 0.4298          | 0.8306   |
| 0.4315        | 5.0   | 265  | 0.3646          | 0.8609   |
| 0.3687        | 6.0   | 318  | 0.3054          | 0.8877   |
| 0.3352        | 7.0   | 371  | 0.2822          | 0.9005   |
| 0.3186        | 8.0   | 424  | 0.2764          | 0.9043   |
| 0.3155        | 9.0   | 477  | 0.2409          | 0.9215   |
| 0.2824        | 10.0  | 530  | 0.2459          | 0.9236   |
| 0.2575        | 11.0  | 583  | 0.2346          | 0.9129   |
| 0.2384        | 12.0  | 636  | 0.2445          | 0.9012   |
| 0.2117        | 13.0  | 689  | 0.1838          | 0.9342   |
| 0.2172        | 14.0  | 742  | 0.1789          | 0.9384   |
| 0.1918        | 15.0  | 795  | 0.1615          | 0.9480   |
| 0.1909        | 16.0  | 848  | 0.1516          | 0.9473   |
| 0.1911        | 17.0  | 901  | 0.1513          | 0.9494   |
| 0.2124        | 18.0  | 954  | 0.1524          | 0.9494   |
| 0.1631        | 19.0  | 1007 | 0.1729          | 0.9339   |
| 0.1766        | 20.0  | 1060 | 0.1329          | 0.9539   |
| 0.168         | 21.0  | 1113 | 0.1235          | 0.9590   |
| 0.1227        | 22.0  | 1166 | 0.1390          | 0.9483   |
| 0.1705        | 23.0  | 1219 | 0.1290          | 0.9566   |
| 0.1296        | 24.0  | 1272 | 0.1119          | 0.9621   |
| 0.1201        | 25.0  | 1325 | 0.1452          | 0.9497   |
| 0.1233        | 26.0  | 1378 | 0.1440          | 0.9487   |
| 0.1412        | 27.0  | 1431 | 0.1206          | 0.9573   |
| 0.1031        | 28.0  | 1484 | 0.1235          | 0.9580   |
| 0.1131        | 29.0  | 1537 | 0.1377          | 0.9501   |
| 0.1157        | 30.0  | 1590 | 0.1308          | 0.9580   |
| 0.0925        | 31.0  | 1643 | 0.1172          | 0.9601   |
| 0.0864        | 32.0  | 1696 | 0.1135          | 0.9621   |
| 0.0748        | 33.0  | 1749 | 0.0987          | 0.9656   |
| 0.1004        | 34.0  | 1802 | 0.0924          | 0.9728   |
| 0.0858        | 35.0  | 1855 | 0.1058          | 0.9659   |
| 0.0976        | 36.0  | 1908 | 0.1180          | 0.9587   |
| 0.0797        | 37.0  | 1961 | 0.1035          | 0.9676   |
| 0.0884        | 38.0  | 2014 | 0.0909          | 0.9707   |
| 0.0841        | 39.0  | 2067 | 0.0979          | 0.9707   |
| 0.0633        | 40.0  | 2120 | 0.0943          | 0.9697   |
| 0.0601        | 41.0  | 2173 | 0.1017          | 0.9687   |
| 0.0693        | 42.0  | 2226 | 0.1160          | 0.9652   |
| 0.0715        | 43.0  | 2279 | 0.0980          | 0.9704   |
| 0.0807        | 44.0  | 2332 | 0.1030          | 0.9711   |
| 0.0614        | 45.0  | 2385 | 0.0999          | 0.9707   |
| 0.0639        | 46.0  | 2438 | 0.1265          | 0.9632   |
| 0.0623        | 47.0  | 2491 | 0.1195          | 0.9614   |
| 0.0444        | 48.0  | 2544 | 0.1338          | 0.9659   |
| 0.0551        | 49.0  | 2597 | 0.1042          | 0.9728   |
| 0.0588        | 50.0  | 2650 | 0.0987          | 0.9731   |
| 0.0421        | 51.0  | 2703 | 0.1306          | 0.9607   |
| 0.0446        | 52.0  | 2756 | 0.1035          | 0.9718   |
| 0.0489        | 53.0  | 2809 | 0.1084          | 0.9714   |
| 0.0529        | 54.0  | 2862 | 0.1225          | 0.9663   |
| 0.0403        | 55.0  | 2915 | 0.1053          | 0.9711   |
| 0.0455        | 56.0  | 2968 | 0.1436          | 0.9645   |
| 0.0436        | 57.0  | 3021 | 0.1052          | 0.9714   |
| 0.0416        | 58.0  | 3074 | 0.1132          | 0.9666   |
| 0.0378        | 59.0  | 3127 | 0.1055          | 0.9721   |
| 0.0545        | 60.0  | 3180 | 0.1166          | 0.9704   |
| 0.0315        | 61.0  | 3233 | 0.1073          | 0.9711   |
| 0.0433        | 62.0  | 3286 | 0.1012          | 0.9735   |
| 0.0577        | 63.0  | 3339 | 0.1117          | 0.9714   |
| 0.0369        | 64.0  | 3392 | 0.1150          | 0.9697   |
| 0.0459        | 65.0  | 3445 | 0.1054          | 0.9731   |
| 0.0458        | 66.0  | 3498 | 0.1045          | 0.9745   |
| 0.0374        | 67.0  | 3551 | 0.1105          | 0.9725   |
| 0.0318        | 68.0  | 3604 | 0.1138          | 0.9718   |
| 0.0337        | 69.0  | 3657 | 0.1053          | 0.9728   |
| 0.0337        | 70.0  | 3710 | 0.1011          | 0.9738   |
| 0.0329        | 71.0  | 3763 | 0.1067          | 0.9738   |
| 0.0313        | 72.0  | 3816 | 0.1003          | 0.9756   |
| 0.0446        | 73.0  | 3869 | 0.1125          | 0.9714   |
| 0.047         | 74.0  | 3922 | 0.1040          | 0.9707   |
| 0.0256        | 75.0  | 3975 | 0.1165          | 0.9700   |
| 0.0535        | 76.0  | 4028 | 0.1129          | 0.9697   |
| 0.029         | 77.0  | 4081 | 0.1040          | 0.9752   |
| 0.044         | 78.0  | 4134 | 0.1116          | 0.9718   |
| 0.0405        | 79.0  | 4187 | 0.1130          | 0.9725   |
| 0.0417        | 80.0  | 4240 | 0.1094          | 0.9735   |
| 0.0257        | 81.0  | 4293 | 0.1143          | 0.9697   |
| 0.0293        | 82.0  | 4346 | 0.1111          | 0.9735   |
| 0.0234        | 83.0  | 4399 | 0.1253          | 0.9704   |
| 0.0295        | 84.0  | 4452 | 0.1133          | 0.9749   |
| 0.0261        | 85.0  | 4505 | 0.1048          | 0.9738   |
| 0.0215        | 86.0  | 4558 | 0.1072          | 0.9728   |
| 0.0304        | 87.0  | 4611 | 0.1061          | 0.9731   |
| 0.02          | 88.0  | 4664 | 0.1072          | 0.9742   |
| 0.0353        | 89.0  | 4717 | 0.1096          | 0.9738   |
| 0.0317        | 90.0  | 4770 | 0.1097          | 0.9745   |
| 0.0441        | 91.0  | 4823 | 0.1080          | 0.9745   |
| 0.0262        | 92.0  | 4876 | 0.1051          | 0.9752   |
| 0.0312        | 93.0  | 4929 | 0.1089          | 0.9738   |
| 0.025         | 94.0  | 4982 | 0.1094          | 0.9738   |
| 0.0243        | 95.0  | 5035 | 0.1106          | 0.9745   |
| 0.0245        | 96.0  | 5088 | 0.1076          | 0.9752   |
| 0.0233        | 97.0  | 5141 | 0.1068          | 0.9762   |
| 0.0279        | 98.0  | 5194 | 0.1063          | 0.9759   |
| 0.0285        | 99.0  | 5247 | 0.1057          | 0.9766   |
| 0.022         | 100.0 | 5300 | 0.1060          | 0.9766   |


### Framework versions

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

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