
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

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: 0.1062
- Accuracy: 0.9749

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.545         | 1.0   | 53   | 1.3735          | 0.4845   |
| 0.8697        | 2.0   | 106  | 0.6895          | 0.7156   |
| 0.5783        | 3.0   | 159  | 0.4415          | 0.8165   |
| 0.4849        | 4.0   | 212  | 0.3737          | 0.8461   |
| 0.4004        | 5.0   | 265  | 0.3442          | 0.8488   |
| 0.3553        | 6.0   | 318  | 0.3271          | 0.8757   |
| 0.3318        | 7.0   | 371  | 0.2491          | 0.9050   |
| 0.3894        | 8.0   | 424  | 0.2636          | 0.9081   |
| 0.3201        | 9.0   | 477  | 0.2368          | 0.9070   |
| 0.2915        | 10.0  | 530  | 0.2390          | 0.9108   |
| 0.2582        | 11.0  | 583  | 0.2044          | 0.9294   |
| 0.2696        | 12.0  | 636  | 0.1948          | 0.9360   |
| 0.2429        | 13.0  | 689  | 0.2282          | 0.9143   |
| 0.257         | 14.0  | 742  | 0.1751          | 0.9339   |
| 0.2042        | 15.0  | 795  | 0.1765          | 0.9349   |
| 0.1952        | 16.0  | 848  | 0.1878          | 0.9284   |
| 0.1949        | 17.0  | 901  | 0.1303          | 0.9494   |
| 0.1786        | 18.0  | 954  | 0.1305          | 0.9552   |
| 0.1593        | 19.0  | 1007 | 0.1249          | 0.9570   |
| 0.1741        | 20.0  | 1060 | 0.1076          | 0.9601   |
| 0.1638        | 21.0  | 1113 | 0.1220          | 0.9580   |
| 0.1261        | 22.0  | 1166 | 0.1344          | 0.9532   |
| 0.1599        | 23.0  | 1219 | 0.1293          | 0.9535   |
| 0.1137        | 24.0  | 1272 | 0.1106          | 0.9621   |
| 0.1257        | 25.0  | 1325 | 0.1205          | 0.9573   |
| 0.1067        | 26.0  | 1378 | 0.1541          | 0.9535   |
| 0.1297        | 27.0  | 1431 | 0.1128          | 0.9604   |
| 0.1076        | 28.0  | 1484 | 0.1092          | 0.9594   |
| 0.0917        | 29.0  | 1537 | 0.1011          | 0.9614   |
| 0.0905        | 30.0  | 1590 | 0.1109          | 0.9604   |
| 0.0948        | 31.0  | 1643 | 0.1046          | 0.9638   |
| 0.0984        | 32.0  | 1696 | 0.1026          | 0.9669   |
| 0.0921        | 33.0  | 1749 | 0.1034          | 0.9642   |
| 0.0762        | 34.0  | 1802 | 0.0925          | 0.9687   |
| 0.0818        | 35.0  | 1855 | 0.0966          | 0.9656   |
| 0.0908        | 36.0  | 1908 | 0.0940          | 0.9687   |
| 0.0699        | 37.0  | 1961 | 0.0779          | 0.9742   |
| 0.0972        | 38.0  | 2014 | 0.1104          | 0.9687   |
| 0.0756        | 39.0  | 2067 | 0.0838          | 0.9742   |
| 0.0878        | 40.0  | 2120 | 0.1119          | 0.9673   |
| 0.0819        | 41.0  | 2173 | 0.1164          | 0.9618   |
| 0.0815        | 42.0  | 2226 | 0.1099          | 0.9666   |
| 0.0618        | 43.0  | 2279 | 0.1003          | 0.9680   |
| 0.0709        | 44.0  | 2332 | 0.0934          | 0.9721   |
| 0.0697        | 45.0  | 2385 | 0.0869          | 0.9731   |
| 0.0551        | 46.0  | 2438 | 0.1086          | 0.9694   |
| 0.049         | 47.0  | 2491 | 0.1036          | 0.9687   |
| 0.0646        | 48.0  | 2544 | 0.0854          | 0.9735   |
| 0.0704        | 49.0  | 2597 | 0.0959          | 0.9714   |
| 0.0578        | 50.0  | 2650 | 0.1034          | 0.9707   |
| 0.0579        | 51.0  | 2703 | 0.0965          | 0.9700   |
| 0.051         | 52.0  | 2756 | 0.0962          | 0.9721   |
| 0.0477        | 53.0  | 2809 | 0.1218          | 0.9690   |
| 0.0769        | 54.0  | 2862 | 0.1027          | 0.9714   |
| 0.0493        | 55.0  | 2915 | 0.1175          | 0.9725   |
| 0.0535        | 56.0  | 2968 | 0.1140          | 0.9690   |
| 0.0359        | 57.0  | 3021 | 0.0990          | 0.9725   |
| 0.0388        | 58.0  | 3074 | 0.0965          | 0.9700   |
| 0.0455        | 59.0  | 3127 | 0.1119          | 0.9700   |
| 0.0584        | 60.0  | 3180 | 0.0989          | 0.9735   |
| 0.0555        | 61.0  | 3233 | 0.1130          | 0.9680   |
| 0.0567        | 62.0  | 3286 | 0.1045          | 0.9721   |
| 0.0543        | 63.0  | 3339 | 0.1168          | 0.9707   |
| 0.0562        | 64.0  | 3392 | 0.1196          | 0.9649   |
| 0.0472        | 65.0  | 3445 | 0.1034          | 0.9725   |
| 0.0387        | 66.0  | 3498 | 0.1125          | 0.9728   |
| 0.0485        | 67.0  | 3551 | 0.1057          | 0.9738   |
| 0.0395        | 68.0  | 3604 | 0.1252          | 0.9725   |
| 0.0266        | 69.0  | 3657 | 0.1023          | 0.9742   |
| 0.0409        | 70.0  | 3710 | 0.1095          | 0.9738   |
| 0.0349        | 71.0  | 3763 | 0.1101          | 0.9752   |
| 0.0205        | 72.0  | 3816 | 0.1127          | 0.9725   |
| 0.0336        | 73.0  | 3869 | 0.1131          | 0.9735   |
| 0.0305        | 74.0  | 3922 | 0.0987          | 0.9749   |
| 0.0298        | 75.0  | 3975 | 0.1051          | 0.9742   |
| 0.0304        | 76.0  | 4028 | 0.1049          | 0.9728   |
| 0.051         | 77.0  | 4081 | 0.1134          | 0.9711   |
| 0.045         | 78.0  | 4134 | 0.1334          | 0.9707   |
| 0.0345        | 79.0  | 4187 | 0.1233          | 0.9707   |
| 0.0328        | 80.0  | 4240 | 0.1106          | 0.9728   |
| 0.0391        | 81.0  | 4293 | 0.1073          | 0.9735   |
| 0.0383        | 82.0  | 4346 | 0.1189          | 0.9707   |
| 0.0299        | 83.0  | 4399 | 0.1131          | 0.9756   |
| 0.0195        | 84.0  | 4452 | 0.1267          | 0.9714   |
| 0.0181        | 85.0  | 4505 | 0.1200          | 0.9700   |
| 0.0266        | 86.0  | 4558 | 0.1086          | 0.9752   |
| 0.0322        | 87.0  | 4611 | 0.1149          | 0.9735   |
| 0.0325        | 88.0  | 4664 | 0.1130          | 0.9738   |
| 0.0303        | 89.0  | 4717 | 0.1105          | 0.9749   |
| 0.0275        | 90.0  | 4770 | 0.1078          | 0.9752   |
| 0.0281        | 91.0  | 4823 | 0.1077          | 0.9742   |
| 0.0231        | 92.0  | 4876 | 0.1060          | 0.9752   |
| 0.022         | 93.0  | 4929 | 0.1077          | 0.9749   |
| 0.0219        | 94.0  | 4982 | 0.1080          | 0.9749   |
| 0.0184        | 95.0  | 5035 | 0.1061          | 0.9756   |
| 0.0198        | 96.0  | 5088 | 0.1047          | 0.9749   |
| 0.0355        | 97.0  | 5141 | 0.1084          | 0.9735   |
| 0.0309        | 98.0  | 5194 | 0.1088          | 0.9735   |
| 0.0324        | 99.0  | 5247 | 0.1066          | 0.9742   |
| 0.0216        | 100.0 | 5300 | 0.1062          | 0.9749   |


### Framework versions

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

downstairs jogging sitting standing upstairs walking