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RobertoSonic
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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV35

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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV35 / README.md
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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-DAV35
    results: []

swinv2-tiny-patch4-window8-256-dmae-humeda-DAV35

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: 3.2578
  • Accuracy: 0.7

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • 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: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.85 1.0 36 1.4133 0.5333
1.9294 2.0 72 0.9294 0.6333
1.1818 3.0 108 0.7700 0.65
0.7534 4.0 144 0.7531 0.7167
0.4285 5.0 180 0.9580 0.7
0.08 6.0 216 1.1785 0.75
0.0891 7.0 252 1.4686 0.7333
0.0602 8.0 288 1.7816 0.7
0.0284 9.0 324 1.5790 0.7667
0.0513 10.0 360 1.8933 0.7
0.0335 11.0 396 2.1433 0.65
0.025 12.0 432 2.3483 0.6667
0.0246 13.0 468 2.6426 0.6667
0.0306 14.0 504 3.0153 0.65
0.016 15.0 540 3.1259 0.6833
0.006 16.0 576 2.7612 0.7167
0.0234 17.0 612 2.5334 0.7167
0.0025 18.0 648 2.1768 0.7667
0.0001 19.0 684 2.6585 0.7167
0.0007 20.0 720 2.3282 0.7167
0.0003 21.0 756 2.6975 0.7333
0.0003 22.0 792 2.6186 0.7
0.0006 23.0 828 2.9600 0.7167
0.0008 24.0 864 2.9623 0.7333
0.0002 25.0 900 2.8632 0.7167
0.0143 26.0 936 2.8460 0.7167
0.0 27.0 972 2.9372 0.7167
0.0002 28.0 1008 2.8056 0.75
0.0001 29.0 1044 3.0591 0.7167
0.0001 30.0 1080 3.3295 0.6833
0.0 31.0 1116 3.2851 0.6833
0.0001 32.0 1152 3.4065 0.7
0.0 33.0 1188 3.3669 0.7
0.0 34.0 1224 3.3185 0.7167
0.0006 35.0 1260 3.2563 0.7
0.0004 36.0 1296 3.2831 0.7
0.0001 37.0 1332 3.2594 0.7
0.0 38.0 1368 3.2576 0.7
0.0 38.9014 1400 3.2578 0.7

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0