swinv2-tiny-patch4-window8-256-DMAE-U2

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

  • Loss: 1.0453
  • Accuracy: 0.6522

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: 3.5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.86 3 1.3843 0.4783
No log 2.0 7 1.3617 0.4565
1.3721 2.86 10 1.3211 0.4565
1.3721 4.0 14 1.2598 0.4565
1.3721 4.86 17 1.2251 0.4565
1.25 6.0 21 1.2094 0.4565
1.25 6.86 24 1.2127 0.4565
1.25 8.0 28 1.2118 0.4565
1.1902 8.86 31 1.2031 0.4565
1.1902 10.0 35 1.1936 0.4565
1.1902 10.86 38 1.1829 0.4565
1.1472 12.0 42 1.1569 0.4565
1.1472 12.86 45 1.1432 0.4565
1.1472 14.0 49 1.1357 0.4783
1.1495 14.86 52 1.1178 0.5
1.1495 16.0 56 1.0903 0.5217
1.1495 16.86 59 1.0714 0.6304
1.0824 18.0 63 1.0453 0.6522
1.0824 18.86 66 1.0150 0.6522
1.0535 20.0 70 0.9925 0.6522
1.0535 20.86 73 0.9778 0.6304
1.0535 22.0 77 0.9570 0.6522
0.994 22.86 80 0.9441 0.6304
0.994 24.0 84 0.9246 0.6304
0.994 24.86 87 0.9095 0.6304
0.9554 26.0 91 0.8937 0.6304
0.9554 26.86 94 0.8925 0.6522
0.9554 28.0 98 0.8886 0.6522
0.953 28.86 101 0.8804 0.6522
0.953 30.0 105 0.8744 0.6522
0.953 30.86 108 0.8754 0.6522
0.9092 32.0 112 0.8733 0.6522
0.9092 32.86 115 0.8725 0.6522
0.9092 34.0 119 0.8722 0.6522
0.9247 34.29 120 0.8720 0.6522

Framework versions

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