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swinv2-tiny-patch4-window8-256-dmae-va-U-40

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: 0.1175
  • Accuracy: 0.9817

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: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9 7 1.3839 0.3028
1.4343 1.94 15 1.3643 0.2844
1.3413 2.97 23 1.3310 0.3578
1.2473 4.0 31 1.1081 0.5138
1.2473 4.9 38 0.8292 0.7064
1.0532 5.94 46 0.7420 0.6239
0.917 6.97 54 0.6345 0.6972
0.7939 8.0 62 0.4898 0.8532
0.7939 8.9 69 0.5918 0.7523
0.7457 9.94 77 0.5271 0.7615
0.6834 10.97 85 0.3296 0.9450
0.5847 12.0 93 0.2883 0.9174
0.5199 12.9 100 0.2896 0.9266
0.5199 13.94 108 0.2859 0.8991
0.4657 14.97 116 0.2515 0.9083
0.4585 16.0 124 0.2261 0.9083
0.3892 16.9 131 0.2142 0.9266
0.3892 17.94 139 0.1788 0.9450
0.3939 18.97 147 0.1948 0.9266
0.3429 20.0 155 0.1685 0.9450
0.3493 20.9 162 0.1986 0.9083
0.3462 21.94 170 0.1540 0.9358
0.3462 22.97 178 0.1449 0.9450
0.3117 24.0 186 0.1379 0.9541
0.3109 24.9 193 0.1423 0.9450
0.2867 25.94 201 0.1451 0.9450
0.2867 26.97 209 0.1154 0.9725
0.293 28.0 217 0.1152 0.9541
0.2782 28.9 224 0.1261 0.9633
0.2744 29.94 232 0.1175 0.9817
0.2711 30.97 240 0.1292 0.9633
0.2711 32.0 248 0.1101 0.9817
0.2652 32.9 255 0.1202 0.9633
0.2218 33.94 263 0.1119 0.9817
0.2899 34.97 271 0.1071 0.9817
0.2899 36.0 279 0.1077 0.9817
0.2143 36.13 280 0.1077 0.9817

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1
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F32
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