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metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-st-mean-wsdmhar-auc
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9703856749311295

swin-tiny-patch4-window7-224-finetuned-st-mean-wsdmhar-auc

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: 0.1076
  • Accuracy: 0.9704

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
1.448 1.0 53 1.2656 0.5530
0.7932 2.0 106 0.6547 0.7018
0.5983 3.0 159 0.4837 0.8106
0.4861 4.0 212 0.3822 0.8564
0.4762 5.0 265 0.3994 0.8140
0.4117 6.0 318 0.3143 0.8822
0.3659 7.0 371 0.2991 0.8819
0.355 8.0 424 0.3318 0.8688
0.3146 9.0 477 0.2399 0.9101
0.3243 10.0 530 0.2115 0.9260
0.3233 11.0 583 0.2022 0.9322
0.2736 12.0 636 0.1983 0.9256
0.2765 13.0 689 0.1739 0.9411
0.2191 14.0 742 0.1701 0.9421
0.2416 15.0 795 0.2053 0.9208
0.2039 16.0 848 0.1674 0.9401
0.2248 17.0 901 0.1700 0.9394
0.2331 18.0 954 0.1722 0.9439
0.1889 19.0 1007 0.1425 0.9470
0.1633 20.0 1060 0.1438 0.9494
0.174 21.0 1113 0.1357 0.9501
0.1599 22.0 1166 0.1346 0.9508
0.155 23.0 1219 0.1318 0.9518
0.1665 24.0 1272 0.1557 0.9477
0.1519 25.0 1325 0.1231 0.9583
0.143 26.0 1378 0.1247 0.9549
0.1393 27.0 1431 0.1615 0.9425
0.1518 28.0 1484 0.1246 0.9580
0.1239 29.0 1537 0.1178 0.9614
0.1297 30.0 1590 0.1141 0.9580
0.165 31.0 1643 0.1353 0.9539
0.1217 32.0 1696 0.1161 0.9621
0.1129 33.0 1749 0.1152 0.9618
0.1125 34.0 1802 0.1185 0.9621
0.1083 35.0 1855 0.1114 0.9642
0.1077 36.0 1908 0.1414 0.9590
0.0875 37.0 1961 0.1360 0.9559
0.1162 38.0 2014 0.1172 0.9618
0.0925 39.0 2067 0.1304 0.9583
0.109 40.0 2120 0.1172 0.9614
0.1178 41.0 2173 0.1525 0.9535
0.0886 42.0 2226 0.1616 0.9487
0.0983 43.0 2279 0.1197 0.9590
0.1209 44.0 2332 0.1183 0.9649
0.0957 45.0 2385 0.1268 0.9597
0.0919 46.0 2438 0.1143 0.9635
0.0831 47.0 2491 0.1319 0.9601
0.0888 48.0 2544 0.1040 0.9707
0.0761 49.0 2597 0.1088 0.9656
0.0843 50.0 2650 0.1046 0.9694
0.0615 51.0 2703 0.0982 0.9652
0.0705 52.0 2756 0.1136 0.9687
0.0775 53.0 2809 0.1272 0.9618
0.0739 54.0 2862 0.1185 0.9676
0.0758 55.0 2915 0.1185 0.9649
0.053 56.0 2968 0.1137 0.9663
0.0675 57.0 3021 0.1150 0.9656
0.0738 58.0 3074 0.1116 0.9676
0.067 59.0 3127 0.1092 0.9687
0.0689 60.0 3180 0.1116 0.9669
0.0647 61.0 3233 0.1107 0.9656
0.0707 62.0 3286 0.1183 0.9673
0.0708 63.0 3339 0.1283 0.9645
0.0675 64.0 3392 0.1222 0.9656
0.0622 65.0 3445 0.1259 0.9669
0.0541 66.0 3498 0.1142 0.9676
0.0528 67.0 3551 0.1103 0.9666
0.0641 68.0 3604 0.1363 0.9652
0.0448 69.0 3657 0.1448 0.9652
0.067 70.0 3710 0.1062 0.9687
0.0674 71.0 3763 0.1065 0.9697
0.0578 72.0 3816 0.1213 0.9669
0.0707 73.0 3869 0.1115 0.9659
0.0666 74.0 3922 0.1115 0.9707
0.0361 75.0 3975 0.1178 0.9680
0.047 76.0 4028 0.1167 0.9718
0.0769 77.0 4081 0.1073 0.9697
0.0422 78.0 4134 0.1116 0.9721
0.0411 79.0 4187 0.1186 0.9676
0.0402 80.0 4240 0.1048 0.9711
0.0504 81.0 4293 0.1105 0.9707
0.0579 82.0 4346 0.1007 0.9704
0.0514 83.0 4399 0.1105 0.9711
0.0398 84.0 4452 0.1130 0.9707
0.0477 85.0 4505 0.1097 0.9718
0.0413 86.0 4558 0.1091 0.9704
0.0538 87.0 4611 0.1068 0.9718
0.043 88.0 4664 0.1104 0.9725
0.0434 89.0 4717 0.1124 0.9707
0.0499 90.0 4770 0.1153 0.9711
0.0418 91.0 4823 0.1121 0.9700
0.0365 92.0 4876 0.1169 0.9711
0.0493 93.0 4929 0.1106 0.9690
0.0426 94.0 4982 0.1089 0.9680
0.0338 95.0 5035 0.1096 0.9711
0.0388 96.0 5088 0.1113 0.9694
0.0404 97.0 5141 0.1102 0.9707
0.0427 98.0 5194 0.1090 0.9704
0.0302 99.0 5247 0.1076 0.9697
0.0404 100.0 5300 0.1076 0.9704

Framework versions

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