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convnextv2-tiny-1k-224-finetuned-fullwear

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convnextv2
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metadata
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: convnextv2-tiny-1k-224-finetuned-fullwear
    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: 0.8402777777777778

convnextv2-tiny-1k-224-finetuned-fullwear

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5203
  • Accuracy: 0.8403

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: 120

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.4871 0.9756 10 2.4771 0.0694
2.4464 1.9512 20 2.4333 0.1528
2.3911 2.9268 30 2.3670 0.2778
2.3204 4.0 41 2.2617 0.3681
2.206 4.9756 51 2.1445 0.3958
2.0869 5.9512 61 2.0146 0.4444
1.9756 6.9268 71 1.8763 0.5139
1.8124 8.0 82 1.7422 0.5486
1.6624 8.9756 92 1.6629 0.5903
1.587 9.9512 102 1.5474 0.6111
1.4746 10.9268 112 1.4577 0.625
1.359 12.0 123 1.3055 0.6736
1.2412 12.9756 133 1.2241 0.6736
1.1374 13.9512 143 1.2003 0.6736
1.0194 14.9268 153 1.0233 0.7569
0.9705 16.0 164 0.9492 0.7847
0.8949 16.9756 174 0.9246 0.75
0.7959 17.9512 184 0.8148 0.7639
0.7491 18.9268 194 0.7858 0.7569
0.6783 20.0 205 0.8010 0.7569
0.6257 20.9756 215 0.7295 0.7847
0.5999 21.9512 225 0.6219 0.8333
0.5701 22.9268 235 0.5932 0.8403
0.4926 24.0 246 0.5970 0.8056
0.4692 24.9756 256 0.6298 0.8194
0.4393 25.9512 266 0.5857 0.8056
0.419 26.9268 276 0.5203 0.8542
0.3454 28.0 287 0.6084 0.8264
0.36 28.9756 297 0.5928 0.8264
0.3265 29.9512 307 0.5303 0.8403
0.3278 30.9268 317 0.6049 0.8194
0.2766 32.0 328 0.5656 0.8264
0.2805 32.9756 338 0.5003 0.8681
0.2505 33.9512 348 0.5412 0.8403
0.2464 34.9268 358 0.5410 0.8333
0.2166 36.0 369 0.5000 0.8472
0.2 36.9756 379 0.5053 0.8056
0.1914 37.9512 389 0.5161 0.8403
0.186 38.9268 399 0.4242 0.8681
0.1592 40.0 410 0.5059 0.8472
0.1598 40.9756 420 0.5143 0.8264
0.1565 41.9512 430 0.4703 0.8542
0.1598 42.9268 440 0.4384 0.8542
0.139 44.0 451 0.4850 0.8403
0.1137 44.9756 461 0.4405 0.8542
0.1158 45.9512 471 0.5250 0.8333
0.1192 46.9268 481 0.5843 0.8194
0.1271 48.0 492 0.4498 0.8611
0.0914 48.9756 502 0.5167 0.8264
0.1079 49.9512 512 0.4648 0.8681
0.091 50.9268 522 0.5321 0.8194
0.1053 52.0 533 0.4402 0.8611
0.0842 52.9756 543 0.4776 0.8542
0.0961 53.9512 553 0.4762 0.8681
0.0896 54.9268 563 0.4477 0.8681
0.0876 56.0 574 0.4951 0.8472
0.0855 56.9756 584 0.5653 0.8125
0.073 57.9512 594 0.5315 0.8472
0.0804 58.9268 604 0.5064 0.8681
0.0765 60.0 615 0.6316 0.8264
0.0782 60.9756 625 0.5733 0.8056
0.069 61.9512 635 0.6994 0.8056
0.0809 62.9268 645 0.4898 0.8611
0.0829 64.0 656 0.6042 0.8194
0.0735 64.9756 666 0.4758 0.8611
0.0763 65.9512 676 0.4921 0.8542
0.0565 66.9268 686 0.4700 0.8681
0.062 68.0 697 0.4944 0.8819
0.0644 68.9756 707 0.4733 0.8681
0.0659 69.9512 717 0.4703 0.8819
0.0625 70.9268 727 0.5075 0.8542
0.042 72.0 738 0.5464 0.8264
0.056 72.9756 748 0.5186 0.8333
0.0858 73.9512 758 0.5403 0.8264
0.0616 74.9268 768 0.5104 0.8472
0.0777 76.0 779 0.5516 0.8403
0.0668 76.9756 789 0.4918 0.8611
0.0585 77.9512 799 0.5692 0.8403
0.0562 78.9268 809 0.5734 0.8403
0.0653 80.0 820 0.5403 0.8264
0.0434 80.9756 830 0.5108 0.8333
0.0483 81.9512 840 0.5699 0.8125
0.0329 82.9268 850 0.6028 0.8056
0.0431 84.0 861 0.5230 0.8333
0.042 84.9756 871 0.5875 0.8194
0.0449 85.9512 881 0.5180 0.8611
0.0512 86.9268 891 0.5425 0.8194
0.0545 88.0 902 0.5690 0.8264
0.0496 88.9756 912 0.5619 0.8611
0.0449 89.9512 922 0.5626 0.8333
0.0405 90.9268 932 0.5267 0.8403
0.0344 92.0 943 0.5617 0.8403
0.0421 92.9756 953 0.5400 0.8611
0.0341 93.9512 963 0.5729 0.8333
0.0492 94.9268 973 0.5855 0.8056
0.0374 96.0 984 0.6113 0.8125
0.0375 96.9756 994 0.5511 0.8403
0.0373 97.9512 1004 0.4942 0.8542
0.0447 98.9268 1014 0.5031 0.8542
0.0519 100.0 1025 0.5349 0.8542
0.0387 100.9756 1035 0.5511 0.8542
0.0256 101.9512 1045 0.5319 0.8403
0.043 102.9268 1055 0.5605 0.8264
0.029 104.0 1066 0.5776 0.8403
0.0379 104.9756 1076 0.5697 0.8472
0.0445 105.9512 1086 0.5133 0.8681
0.0267 106.9268 1096 0.5076 0.8681
0.044 108.0 1107 0.5260 0.8403
0.0263 108.9756 1117 0.5101 0.8542
0.0247 109.9512 1127 0.4972 0.8542
0.0441 110.9268 1137 0.5094 0.8472
0.0263 112.0 1148 0.5259 0.8333
0.0247 112.9756 1158 0.5323 0.8403
0.0356 113.9512 1168 0.5275 0.8403
0.0297 114.9268 1178 0.5240 0.8333
0.044 116.0 1189 0.5201 0.8472
0.031 116.9756 1199 0.5203 0.8403
0.0369 117.0732 1200 0.5203 0.8403

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1