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metadata
library_name: transformers
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-dmae-humeda-DAV37
    results: []

swinv2-tiny-patch4-window8-256-dmae-humeda-DAV37

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: 1.8351
  • Accuracy: 0.6538

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.8 3 1.6284 0.1346
No log 1.8 6 1.5966 0.2404
No log 2.8 9 1.5076 0.3942
6.28 3.8 12 1.2912 0.4615
6.28 4.8 15 1.2137 0.5096
6.28 5.8 18 1.1917 0.5385
6.28 6.8 21 1.1498 0.5673
2.9539 7.8 24 1.2026 0.5865
2.9539 8.8 27 1.2711 0.5962
2.9539 9.8 30 1.3534 0.625
2.9539 10.8 33 1.3210 0.625
0.9643 11.8 36 1.3940 0.6346
0.9643 12.8 39 1.4859 0.6346
0.9643 13.8 42 1.4965 0.6346
0.9643 14.8 45 1.5463 0.625
0.3275 15.8 48 1.5885 0.6346
0.3275 16.8 51 1.6466 0.6442
0.3275 17.8 54 1.8351 0.6538
0.3275 18.8 57 1.8326 0.6442
0.1501 19.8 60 1.7521 0.6346
0.1501 20.8 63 1.7806 0.6538
0.1501 21.8 66 1.7669 0.6538
0.1501 22.8 69 1.8874 0.6346
0.09 23.8 72 1.8827 0.6538
0.09 24.8 75 1.8330 0.6538
0.09 25.8 78 1.8331 0.6538
0.09 26.8 81 1.8410 0.6538
0.0595 27.8 84 1.8441 0.6442
0.0595 28.8 87 1.8444 0.6538
0.0595 29.8 90 1.8446 0.6538

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0