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

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: 0.1144
- Accuracy: 0.9711

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4641        | 1.0   | 53   | 1.2510          | 0.5768   |
| 0.7713        | 2.0   | 106  | 0.6282          | 0.7435   |
| 0.5822        | 3.0   | 159  | 0.4503          | 0.8251   |
| 0.5086        | 4.0   | 212  | 0.4028          | 0.8306   |
| 0.4499        | 5.0   | 265  | 0.3399          | 0.8709   |
| 0.4171        | 6.0   | 318  | 0.3024          | 0.8898   |
| 0.3597        | 7.0   | 371  | 0.2584          | 0.9029   |
| 0.3201        | 8.0   | 424  | 0.2467          | 0.9143   |
| 0.2974        | 9.0   | 477  | 0.2784          | 0.8981   |
| 0.3323        | 10.0  | 530  | 0.2414          | 0.9101   |
| 0.2717        | 11.0  | 583  | 0.2051          | 0.9349   |
| 0.2647        | 12.0  | 636  | 0.1944          | 0.9294   |
| 0.296         | 13.0  | 689  | 0.1871          | 0.9329   |
| 0.2434        | 14.0  | 742  | 0.1701          | 0.9411   |
| 0.2293        | 15.0  | 795  | 0.1685          | 0.9435   |
| 0.2196        | 16.0  | 848  | 0.1486          | 0.9446   |
| 0.2249        | 17.0  | 901  | 0.1438          | 0.9456   |
| 0.2142        | 18.0  | 954  | 0.1529          | 0.9449   |
| 0.2024        | 19.0  | 1007 | 0.1361          | 0.9532   |
| 0.2177        | 20.0  | 1060 | 0.1906          | 0.9315   |
| 0.1812        | 21.0  | 1113 | 0.1333          | 0.9532   |
| 0.1771        | 22.0  | 1166 | 0.1424          | 0.9528   |
| 0.1718        | 23.0  | 1219 | 0.1370          | 0.9545   |
| 0.1333        | 24.0  | 1272 | 0.1550          | 0.9466   |
| 0.1592        | 25.0  | 1325 | 0.1344          | 0.9535   |
| 0.1279        | 26.0  | 1378 | 0.1201          | 0.9590   |
| 0.1547        | 27.0  | 1431 | 0.1225          | 0.9597   |
| 0.1443        | 28.0  | 1484 | 0.1526          | 0.9480   |
| 0.1306        | 29.0  | 1537 | 0.1188          | 0.9597   |
| 0.129         | 30.0  | 1590 | 0.1204          | 0.9614   |
| 0.1354        | 31.0  | 1643 | 0.1351          | 0.9570   |
| 0.135         | 32.0  | 1696 | 0.1010          | 0.9676   |
| 0.137         | 33.0  | 1749 | 0.1381          | 0.9566   |
| 0.101         | 34.0  | 1802 | 0.1119          | 0.9642   |
| 0.1118        | 35.0  | 1855 | 0.1056          | 0.9656   |
| 0.1206        | 36.0  | 1908 | 0.0975          | 0.9663   |
| 0.1028        | 37.0  | 1961 | 0.1265          | 0.9635   |
| 0.1058        | 38.0  | 2014 | 0.0958          | 0.9680   |
| 0.1013        | 39.0  | 2067 | 0.1060          | 0.9642   |
| 0.0765        | 40.0  | 2120 | 0.1024          | 0.9659   |
| 0.0997        | 41.0  | 2173 | 0.1116          | 0.9642   |
| 0.0933        | 42.0  | 2226 | 0.1082          | 0.9666   |
| 0.0937        | 43.0  | 2279 | 0.1095          | 0.9680   |
| 0.0831        | 44.0  | 2332 | 0.1034          | 0.9683   |
| 0.0849        | 45.0  | 2385 | 0.0995          | 0.9690   |
| 0.0739        | 46.0  | 2438 | 0.1042          | 0.9666   |
| 0.0944        | 47.0  | 2491 | 0.1068          | 0.9697   |
| 0.0821        | 48.0  | 2544 | 0.1087          | 0.9690   |
| 0.0835        | 49.0  | 2597 | 0.0975          | 0.9721   |
| 0.0666        | 50.0  | 2650 | 0.1189          | 0.9638   |
| 0.0614        | 51.0  | 2703 | 0.1421          | 0.9611   |
| 0.0738        | 52.0  | 2756 | 0.1253          | 0.9638   |
| 0.0949        | 53.0  | 2809 | 0.1274          | 0.9663   |
| 0.068         | 54.0  | 2862 | 0.1051          | 0.9669   |
| 0.0626        | 55.0  | 2915 | 0.1102          | 0.9673   |
| 0.0647        | 56.0  | 2968 | 0.1096          | 0.9673   |
| 0.0803        | 57.0  | 3021 | 0.1049          | 0.9683   |
| 0.0744        | 58.0  | 3074 | 0.1039          | 0.9697   |
| 0.0769        | 59.0  | 3127 | 0.1060          | 0.9690   |
| 0.0763        | 60.0  | 3180 | 0.1077          | 0.9680   |
| 0.0591        | 61.0  | 3233 | 0.1165          | 0.9680   |
| 0.0649        | 62.0  | 3286 | 0.1109          | 0.9694   |
| 0.0557        | 63.0  | 3339 | 0.1162          | 0.9680   |
| 0.0644        | 64.0  | 3392 | 0.1039          | 0.9718   |
| 0.0558        | 65.0  | 3445 | 0.1182          | 0.9687   |
| 0.0633        | 66.0  | 3498 | 0.1151          | 0.9680   |
| 0.0586        | 67.0  | 3551 | 0.1147          | 0.9694   |
| 0.0651        | 68.0  | 3604 | 0.1124          | 0.9711   |
| 0.0693        | 69.0  | 3657 | 0.1104          | 0.9687   |
| 0.0584        | 70.0  | 3710 | 0.1177          | 0.9697   |
| 0.0471        | 71.0  | 3763 | 0.1160          | 0.9690   |
| 0.0614        | 72.0  | 3816 | 0.1220          | 0.9680   |
| 0.0583        | 73.0  | 3869 | 0.1236          | 0.9656   |
| 0.0495        | 74.0  | 3922 | 0.1076          | 0.9718   |
| 0.0574        | 75.0  | 3975 | 0.1163          | 0.9673   |
| 0.0399        | 76.0  | 4028 | 0.1126          | 0.9683   |
| 0.0357        | 77.0  | 4081 | 0.1064          | 0.9728   |
| 0.0441        | 78.0  | 4134 | 0.1139          | 0.9694   |
| 0.0504        | 79.0  | 4187 | 0.1083          | 0.9707   |
| 0.0546        | 80.0  | 4240 | 0.1167          | 0.9676   |
| 0.0528        | 81.0  | 4293 | 0.1143          | 0.9697   |
| 0.0385        | 82.0  | 4346 | 0.1226          | 0.9676   |
| 0.0511        | 83.0  | 4399 | 0.1199          | 0.9694   |
| 0.0533        | 84.0  | 4452 | 0.1279          | 0.9673   |
| 0.043         | 85.0  | 4505 | 0.1161          | 0.9714   |
| 0.0231        | 86.0  | 4558 | 0.1166          | 0.9728   |
| 0.0426        | 87.0  | 4611 | 0.1239          | 0.9690   |
| 0.0565        | 88.0  | 4664 | 0.1189          | 0.9687   |
| 0.0378        | 89.0  | 4717 | 0.1186          | 0.9697   |
| 0.0406        | 90.0  | 4770 | 0.1209          | 0.9718   |
| 0.0306        | 91.0  | 4823 | 0.1189          | 0.9721   |
| 0.0354        | 92.0  | 4876 | 0.1244          | 0.9687   |
| 0.0293        | 93.0  | 4929 | 0.1235          | 0.9697   |
| 0.0381        | 94.0  | 4982 | 0.1186          | 0.9711   |
| 0.0372        | 95.0  | 5035 | 0.1172          | 0.9714   |
| 0.0469        | 96.0  | 5088 | 0.1180          | 0.9711   |
| 0.0535        | 97.0  | 5141 | 0.1152          | 0.9718   |
| 0.0496        | 98.0  | 5194 | 0.1157          | 0.9714   |
| 0.034         | 99.0  | 5247 | 0.1145          | 0.9714   |
| 0.0348        | 100.0 | 5300 | 0.1144          | 0.9711   |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1

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