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swinv2-tiny-patch4-window8-256-ve-UH2

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metadata
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: swinv2-tiny-patch4-window8-256-ve-UH2
    results: []

swinv2-tiny-patch4-window8-256-ve-UH2

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7448
  • Accuracy: 0.7308

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: 80

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 2 1.6091 0.4038
No log 2.0 4 1.6070 0.4231
No log 3.0 6 1.6017 0.4038
No log 4.0 8 1.5911 0.4038
1.6022 5.0 10 1.5707 0.4038
1.6022 6.0 12 1.5355 0.4038
1.6022 7.0 14 1.4951 0.4038
1.6022 8.0 16 1.4528 0.4038
1.6022 9.0 18 1.4096 0.4038
1.4645 10.0 20 1.3820 0.4038
1.4645 11.0 22 1.4055 0.4038
1.4645 12.0 24 1.3765 0.4038
1.4645 13.0 26 1.3820 0.4038
1.4645 14.0 28 1.3712 0.4038
1.3172 15.0 30 1.3546 0.4038
1.3172 16.0 32 1.3637 0.4038
1.3172 17.0 34 1.3646 0.4038
1.3172 18.0 36 1.3271 0.4038
1.3172 19.0 38 1.3084 0.4038
1.2549 20.0 40 1.3402 0.4038
1.2549 21.0 42 1.3550 0.4038
1.2549 22.0 44 1.2677 0.4038
1.2549 23.0 46 1.2093 0.4038
1.2549 24.0 48 1.2040 0.4231
1.2092 25.0 50 1.2963 0.4231
1.2092 26.0 52 1.2917 0.4808
1.2092 27.0 54 1.1798 0.5769
1.2092 28.0 56 1.1047 0.6346
1.2092 29.0 58 1.0923 0.6731
1.1321 30.0 60 1.1066 0.6538
1.1321 31.0 62 1.0874 0.6538
1.1321 32.0 64 1.0548 0.6731
1.1321 33.0 66 1.0012 0.6538
1.1321 34.0 68 0.9641 0.6923
1.0022 35.0 70 0.9796 0.6538
1.0022 36.0 72 0.9631 0.6538
1.0022 37.0 74 0.9040 0.6731
1.0022 38.0 76 0.8731 0.6923
1.0022 39.0 78 0.8960 0.6731
0.8941 40.0 80 0.9133 0.6538
0.8941 41.0 82 0.8507 0.6923
0.8941 42.0 84 0.8064 0.7115
0.8941 43.0 86 0.8075 0.7115
0.8941 44.0 88 0.8486 0.6923
0.7866 45.0 90 0.8075 0.6923
0.7866 46.0 92 0.7496 0.6731
0.7866 47.0 94 0.7431 0.6731
0.7866 48.0 96 0.7442 0.6731
0.7866 49.0 98 0.7735 0.6923
0.7281 50.0 100 0.7751 0.6923
0.7281 51.0 102 0.7370 0.6923
0.7281 52.0 104 0.7230 0.6923
0.7281 53.0 106 0.7314 0.6923
0.7281 54.0 108 0.7498 0.6731
0.6725 55.0 110 0.7557 0.6731
0.6725 56.0 112 0.7314 0.7115
0.6725 57.0 114 0.7334 0.7115
0.6725 58.0 116 0.7375 0.7115
0.6725 59.0 118 0.7434 0.6923
0.6526 60.0 120 0.7548 0.6731
0.6526 61.0 122 0.7813 0.7115
0.6526 62.0 124 0.7722 0.6923
0.6526 63.0 126 0.7469 0.6923
0.6526 64.0 128 0.7402 0.6731
0.5915 65.0 130 0.7448 0.7308
0.5915 66.0 132 0.7467 0.6923
0.5915 67.0 134 0.7496 0.6731
0.5915 68.0 136 0.7518 0.7308
0.5915 69.0 138 0.7453 0.7115
0.578 70.0 140 0.7385 0.6923
0.578 71.0 142 0.7411 0.6731
0.578 72.0 144 0.7442 0.6731
0.578 73.0 146 0.7440 0.6731
0.578 74.0 148 0.7428 0.6923
0.5826 75.0 150 0.7414 0.6923
0.5826 76.0 152 0.7416 0.6923
0.5826 77.0 154 0.7414 0.6923
0.5826 78.0 156 0.7413 0.6731
0.5826 79.0 158 0.7413 0.6731
0.5586 80.0 160 0.7415 0.6923

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0