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hushem_1x_deit_base_adamax_00001_fold2

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

  • Loss: 1.2691
  • Accuracy: 0.5556

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: 1e-05
  • 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 1.3747 0.2222
1.3344 2.0 12 1.3328 0.3333
1.3344 3.0 18 1.2950 0.3778
1.1589 4.0 24 1.2639 0.4222
1.0046 5.0 30 1.2362 0.4667
1.0046 6.0 36 1.2118 0.5111
0.8105 7.0 42 1.1924 0.5111
0.8105 8.0 48 1.1812 0.5111
0.6759 9.0 54 1.1724 0.4889
0.5237 10.0 60 1.1564 0.5333
0.5237 11.0 66 1.1490 0.5333
0.4159 12.0 72 1.1389 0.5333
0.4159 13.0 78 1.1320 0.5556
0.3256 14.0 84 1.1287 0.5333
0.2501 15.0 90 1.1375 0.5333
0.2501 16.0 96 1.1291 0.5333
0.2011 17.0 102 1.1342 0.5333
0.2011 18.0 108 1.1537 0.5333
0.1629 19.0 114 1.1612 0.5333
0.1297 20.0 120 1.1554 0.5333
0.1297 21.0 126 1.1646 0.5333
0.105 22.0 132 1.1740 0.5333
0.105 23.0 138 1.1835 0.5333
0.0899 24.0 144 1.1913 0.5333
0.0683 25.0 150 1.2006 0.5333
0.0683 26.0 156 1.2072 0.5556
0.0612 27.0 162 1.2095 0.5556
0.0612 28.0 168 1.2218 0.5556
0.0476 29.0 174 1.2356 0.5556
0.0433 30.0 180 1.2335 0.5556
0.0433 31.0 186 1.2371 0.5556
0.0387 32.0 192 1.2442 0.5556
0.0387 33.0 198 1.2507 0.5556
0.0349 34.0 204 1.2533 0.5556
0.0342 35.0 210 1.2544 0.5556
0.0342 36.0 216 1.2556 0.5556
0.0314 37.0 222 1.2581 0.5556
0.0314 38.0 228 1.2633 0.5556
0.0273 39.0 234 1.2676 0.5556
0.0293 40.0 240 1.2685 0.5556
0.0293 41.0 246 1.2690 0.5556
0.0278 42.0 252 1.2691 0.5556
0.0278 43.0 258 1.2691 0.5556
0.0273 44.0 264 1.2691 0.5556
0.0276 45.0 270 1.2691 0.5556
0.0276 46.0 276 1.2691 0.5556
0.0276 47.0 282 1.2691 0.5556
0.0276 48.0 288 1.2691 0.5556
0.0275 49.0 294 1.2691 0.5556
0.0279 50.0 300 1.2691 0.5556

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.7
  • Tokenizers 0.15.0
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Evaluation results