swinv2-tiny-patch4-window8-256-DMAE-U4

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: 1.5677
  • Accuracy: 0.4565

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: 4.5e-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 7.9349 0.1087
No log 2.0 7 7.8133 0.1087
7.8893 2.86 10 7.4996 0.1087
7.8893 4.0 14 6.8656 0.1087
7.8893 4.86 17 6.3003 0.1087
6.7417 6.0 21 5.5397 0.1087
6.7417 6.86 24 5.0339 0.1087
6.7417 8.0 28 4.3869 0.1087
4.9507 8.86 31 3.9219 0.1087
4.9507 10.0 35 3.3482 0.1087
4.9507 10.86 38 2.9557 0.1087
3.5754 12.0 42 2.4984 0.1087
3.5754 12.86 45 2.2140 0.1087
3.5754 14.0 49 1.9120 0.1087
2.3517 14.86 52 1.7396 0.1087
2.3517 16.0 56 1.5677 0.4565
2.3517 16.86 59 1.4687 0.4565
1.65 18.0 63 1.3626 0.4565
1.65 18.86 66 1.3022 0.4565
1.3511 20.0 70 1.2465 0.4565
1.3511 20.86 73 1.2214 0.4565
1.3511 22.0 77 1.2086 0.4565
1.2045 22.86 80 1.2084 0.4565
1.2045 24.0 84 1.2099 0.4565
1.2045 24.86 87 1.2104 0.4565
1.1908 26.0 91 1.2096 0.4565
1.1908 26.86 94 1.2095 0.4565
1.1908 28.0 98 1.2087 0.4565
1.201 28.86 101 1.2076 0.4565
1.201 30.0 105 1.2078 0.4565
1.201 30.86 108 1.2081 0.4565
1.1807 32.0 112 1.2082 0.4565
1.1807 32.86 115 1.2082 0.4565
1.1807 34.0 119 1.2082 0.4565
1.2091 34.29 120 1.2082 0.4565

Framework versions

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