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hushem_1x_deit_base_adamax_0001_fold1

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: 1.0735
  • Accuracy: 0.7556

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 1.2850 0.3333
1.0598 2.0 12 0.9334 0.6889
1.0598 3.0 18 0.8053 0.7333
0.3175 4.0 24 0.8711 0.6444
0.0445 5.0 30 0.8608 0.7556
0.0445 6.0 36 0.7787 0.7778
0.0064 7.0 42 0.8229 0.7111
0.0064 8.0 48 0.9560 0.6667
0.0019 9.0 54 1.0123 0.7111
0.001 10.0 60 1.0574 0.7111
0.001 11.0 66 1.0683 0.7111
0.0007 12.0 72 1.0663 0.7111
0.0007 13.0 78 1.0586 0.7111
0.0005 14.0 84 1.0513 0.7333
0.0005 15.0 90 1.0471 0.7333
0.0005 16.0 96 1.0473 0.7333
0.0004 17.0 102 1.0477 0.7333
0.0004 18.0 108 1.0472 0.7333
0.0004 19.0 114 1.0473 0.7333
0.0004 20.0 120 1.0490 0.7556
0.0004 21.0 126 1.0517 0.7556
0.0004 22.0 132 1.0538 0.7556
0.0004 23.0 138 1.0557 0.7556
0.0003 24.0 144 1.0576 0.7556
0.0003 25.0 150 1.0597 0.7556
0.0003 26.0 156 1.0622 0.7556
0.0003 27.0 162 1.0623 0.7556
0.0003 28.0 168 1.0636 0.7556
0.0003 29.0 174 1.0651 0.7556
0.0003 30.0 180 1.0659 0.7556
0.0003 31.0 186 1.0671 0.7556
0.0003 32.0 192 1.0678 0.7556
0.0003 33.0 198 1.0692 0.7556
0.0003 34.0 204 1.0702 0.7556
0.0003 35.0 210 1.0706 0.7556
0.0003 36.0 216 1.0707 0.7556
0.0003 37.0 222 1.0711 0.7556
0.0003 38.0 228 1.0722 0.7556
0.0003 39.0 234 1.0729 0.7556
0.0003 40.0 240 1.0733 0.7556
0.0003 41.0 246 1.0735 0.7556
0.0003 42.0 252 1.0735 0.7556
0.0003 43.0 258 1.0735 0.7556
0.0003 44.0 264 1.0735 0.7556
0.0003 45.0 270 1.0735 0.7556
0.0003 46.0 276 1.0735 0.7556
0.0003 47.0 282 1.0735 0.7556
0.0003 48.0 288 1.0735 0.7556
0.0003 49.0 294 1.0735 0.7556
0.0003 50.0 300 1.0735 0.7556

Framework versions

  • Transformers 4.35.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.7
  • Tokenizers 0.14.1
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Evaluation results