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

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: 0.1344
- Accuracy: 0.9680

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4973        | 1.0   | 53   | 1.3214          | 0.3743   |
| 0.7898        | 2.0   | 106  | 0.7212          | 0.6753   |
| 0.5919        | 3.0   | 159  | 0.5983          | 0.7317   |
| 0.5388        | 4.0   | 212  | 0.4451          | 0.8209   |
| 0.475         | 5.0   | 265  | 0.3542          | 0.8674   |
| 0.4174        | 6.0   | 318  | 0.3148          | 0.8771   |
| 0.3487        | 7.0   | 371  | 0.3107          | 0.8802   |
| 0.3385        | 8.0   | 424  | 0.3179          | 0.8798   |
| 0.3324        | 9.0   | 477  | 0.2846          | 0.8998   |
| 0.3347        | 10.0  | 530  | 0.2837          | 0.8871   |
| 0.2952        | 11.0  | 583  | 0.2412          | 0.9139   |
| 0.282         | 12.0  | 636  | 0.3142          | 0.8767   |
| 0.2679        | 13.0  | 689  | 0.2496          | 0.9005   |
| 0.2816        | 14.0  | 742  | 0.2014          | 0.9239   |
| 0.2989        | 15.0  | 795  | 0.2049          | 0.9218   |
| 0.2634        | 16.0  | 848  | 0.2066          | 0.9232   |
| 0.2692        | 17.0  | 901  | 0.1994          | 0.9284   |
| 0.2069        | 18.0  | 954  | 0.1958          | 0.9304   |
| 0.2373        | 19.0  | 1007 | 0.2273          | 0.9249   |
| 0.1992        | 20.0  | 1060 | 0.2094          | 0.9267   |
| 0.1997        | 21.0  | 1113 | 0.1808          | 0.9387   |
| 0.1794        | 22.0  | 1166 | 0.1833          | 0.9408   |
| 0.1736        | 23.0  | 1219 | 0.2456          | 0.9091   |
| 0.2004        | 24.0  | 1272 | 0.1918          | 0.9294   |
| 0.2039        | 25.0  | 1325 | 0.1768          | 0.9370   |
| 0.1829        | 26.0  | 1378 | 0.2090          | 0.9225   |
| 0.1566        | 27.0  | 1431 | 0.1467          | 0.9456   |
| 0.1531        | 28.0  | 1484 | 0.1604          | 0.9404   |
| 0.1553        | 29.0  | 1537 | 0.1612          | 0.9449   |
| 0.1406        | 30.0  | 1590 | 0.1644          | 0.9494   |
| 0.1396        | 31.0  | 1643 | 0.1411          | 0.9501   |
| 0.1049        | 32.0  | 1696 | 0.1616          | 0.9539   |
| 0.1411        | 33.0  | 1749 | 0.1708          | 0.9446   |
| 0.1211        | 34.0  | 1802 | 0.1392          | 0.9501   |
| 0.1113        | 35.0  | 1855 | 0.1369          | 0.9525   |
| 0.1249        | 36.0  | 1908 | 0.1320          | 0.9535   |
| 0.1274        | 37.0  | 1961 | 0.1524          | 0.9518   |
| 0.1191        | 38.0  | 2014 | 0.1438          | 0.9525   |
| 0.0949        | 39.0  | 2067 | 0.1379          | 0.9573   |
| 0.0936        | 40.0  | 2120 | 0.1463          | 0.9518   |
| 0.1008        | 41.0  | 2173 | 0.1681          | 0.9494   |
| 0.0887        | 42.0  | 2226 | 0.1463          | 0.9566   |
| 0.1113        | 43.0  | 2279 | 0.1719          | 0.9456   |
| 0.1087        | 44.0  | 2332 | 0.1343          | 0.9604   |
| 0.097         | 45.0  | 2385 | 0.1431          | 0.9576   |
| 0.1061        | 46.0  | 2438 | 0.1495          | 0.9580   |
| 0.11          | 47.0  | 2491 | 0.1555          | 0.9549   |
| 0.0806        | 48.0  | 2544 | 0.1493          | 0.9549   |
| 0.0979        | 49.0  | 2597 | 0.2320          | 0.9373   |
| 0.0751        | 50.0  | 2650 | 0.1516          | 0.9573   |
| 0.0845        | 51.0  | 2703 | 0.1277          | 0.9614   |
| 0.079         | 52.0  | 2756 | 0.1373          | 0.9601   |
| 0.0818        | 53.0  | 2809 | 0.1569          | 0.9539   |
| 0.0845        | 54.0  | 2862 | 0.1422          | 0.9604   |
| 0.0796        | 55.0  | 2915 | 0.1400          | 0.9621   |
| 0.0975        | 56.0  | 2968 | 0.1375          | 0.9573   |
| 0.0607        | 57.0  | 3021 | 0.1504          | 0.9580   |
| 0.0632        | 58.0  | 3074 | 0.1364          | 0.9607   |
| 0.0542        | 59.0  | 3127 | 0.1278          | 0.9669   |
| 0.0807        | 60.0  | 3180 | 0.1507          | 0.9518   |
| 0.0673        | 61.0  | 3233 | 0.1302          | 0.9645   |
| 0.0773        | 62.0  | 3286 | 0.1388          | 0.9638   |
| 0.0739        | 63.0  | 3339 | 0.1533          | 0.9573   |
| 0.0718        | 64.0  | 3392 | 0.1325          | 0.9594   |
| 0.0719        | 65.0  | 3445 | 0.1304          | 0.9625   |
| 0.0487        | 66.0  | 3498 | 0.1250          | 0.9645   |
| 0.0718        | 67.0  | 3551 | 0.1512          | 0.9573   |
| 0.0851        | 68.0  | 3604 | 0.1299          | 0.9607   |
| 0.0658        | 69.0  | 3657 | 0.1424          | 0.9625   |
| 0.0605        | 70.0  | 3710 | 0.1391          | 0.9625   |
| 0.0732        | 71.0  | 3763 | 0.1320          | 0.9642   |
| 0.0613        | 72.0  | 3816 | 0.1461          | 0.9607   |
| 0.056         | 73.0  | 3869 | 0.1328          | 0.9635   |
| 0.0661        | 74.0  | 3922 | 0.1319          | 0.9628   |
| 0.0581        | 75.0  | 3975 | 0.1337          | 0.9666   |
| 0.0698        | 76.0  | 4028 | 0.1383          | 0.9645   |
| 0.0544        | 77.0  | 4081 | 0.1324          | 0.9656   |
| 0.059         | 78.0  | 4134 | 0.1380          | 0.9645   |
| 0.0554        | 79.0  | 4187 | 0.1435          | 0.9638   |
| 0.0497        | 80.0  | 4240 | 0.1310          | 0.9649   |
| 0.0463        | 81.0  | 4293 | 0.1384          | 0.9604   |
| 0.0622        | 82.0  | 4346 | 0.1363          | 0.9628   |
| 0.0534        | 83.0  | 4399 | 0.1428          | 0.9635   |
| 0.0434        | 84.0  | 4452 | 0.1374          | 0.9656   |
| 0.0591        | 85.0  | 4505 | 0.1332          | 0.9663   |
| 0.0488        | 86.0  | 4558 | 0.1271          | 0.9697   |
| 0.0418        | 87.0  | 4611 | 0.1286          | 0.9669   |
| 0.0505        | 88.0  | 4664 | 0.1372          | 0.9676   |
| 0.0486        | 89.0  | 4717 | 0.1372          | 0.9676   |
| 0.0561        | 90.0  | 4770 | 0.1348          | 0.9680   |
| 0.0498        | 91.0  | 4823 | 0.1340          | 0.9669   |
| 0.0432        | 92.0  | 4876 | 0.1351          | 0.9621   |
| 0.0322        | 93.0  | 4929 | 0.1380          | 0.9659   |
| 0.0389        | 94.0  | 4982 | 0.1370          | 0.9656   |
| 0.0408        | 95.0  | 5035 | 0.1343          | 0.9683   |
| 0.0367        | 96.0  | 5088 | 0.1347          | 0.9680   |
| 0.0337        | 97.0  | 5141 | 0.1366          | 0.9669   |
| 0.0338        | 98.0  | 5194 | 0.1355          | 0.9669   |
| 0.0284        | 99.0  | 5247 | 0.1340          | 0.9676   |
| 0.0501        | 100.0 | 5300 | 0.1344          | 0.9680   |


### Framework versions

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

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