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

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

This model is a fine-tuned version of 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
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Evaluation results

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