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hushem_1x_deit_tiny_rms_lr0001_fold4

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2563
  • Accuracy: 0.7619

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 6 1.5375 0.2619
2.1724 2.0 12 2.0732 0.2381
2.1724 3.0 18 1.4131 0.2619
1.6782 4.0 24 1.3425 0.4524
1.4417 5.0 30 1.3458 0.2857
1.4417 6.0 36 1.2594 0.6429
1.3676 7.0 42 1.1740 0.4762
1.3676 8.0 48 1.2511 0.3571
1.2512 9.0 54 0.8438 0.6190
0.8279 10.0 60 1.0096 0.5
0.8279 11.0 66 0.7631 0.6667
0.5322 12.0 72 0.6526 0.7857
0.5322 13.0 78 0.6963 0.7143
0.257 14.0 84 0.7429 0.7619
0.1198 15.0 90 0.9632 0.6905
0.1198 16.0 96 1.2325 0.7143
0.0178 17.0 102 1.2090 0.7381
0.0178 18.0 108 1.1054 0.7619
0.0016 19.0 114 1.2184 0.7143
0.0009 20.0 120 1.1716 0.7619
0.0009 21.0 126 1.1784 0.7619
0.0004 22.0 132 1.1866 0.7619
0.0004 23.0 138 1.1935 0.7619
0.0003 24.0 144 1.1995 0.7619
0.0003 25.0 150 1.2046 0.7619
0.0003 26.0 156 1.2111 0.7619
0.0003 27.0 162 1.2169 0.7619
0.0003 28.0 168 1.2218 0.7619
0.0002 29.0 174 1.2261 0.7619
0.0002 30.0 180 1.2318 0.7619
0.0002 31.0 186 1.2354 0.7619
0.0002 32.0 192 1.2392 0.7619
0.0002 33.0 198 1.2423 0.7619
0.0002 34.0 204 1.2453 0.7619
0.0002 35.0 210 1.2477 0.7619
0.0002 36.0 216 1.2499 0.7619
0.0002 37.0 222 1.2519 0.7619
0.0002 38.0 228 1.2534 0.7619
0.0002 39.0 234 1.2547 0.7619
0.0002 40.0 240 1.2556 0.7619
0.0002 41.0 246 1.2562 0.7619
0.0002 42.0 252 1.2563 0.7619
0.0002 43.0 258 1.2563 0.7619
0.0002 44.0 264 1.2563 0.7619
0.0002 45.0 270 1.2563 0.7619
0.0002 46.0 276 1.2563 0.7619
0.0002 47.0 282 1.2563 0.7619
0.0002 48.0 288 1.2563 0.7619
0.0002 49.0 294 1.2563 0.7619
0.0002 50.0 300 1.2563 0.7619

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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Evaluation results