hushem_1x_deit_tiny_rms_0001_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: 2.3903
  • Accuracy: 0.5714

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 1.5699 0.2619
2.0801 2.0 12 1.5693 0.2381
2.0801 3.0 18 1.6087 0.2619
1.5352 4.0 24 1.4372 0.2619
1.4323 5.0 30 1.3212 0.3095
1.4323 6.0 36 1.3803 0.2381
1.3894 7.0 42 1.4606 0.4524
1.3894 8.0 48 1.5543 0.2619
1.294 9.0 54 1.1365 0.5
1.1627 10.0 60 1.3219 0.3571
1.1627 11.0 66 1.0508 0.5714
1.0159 12.0 72 1.0736 0.5
1.0159 13.0 78 1.6175 0.3571
0.8051 14.0 84 1.4409 0.4524
0.5869 15.0 90 2.1188 0.4286
0.5869 16.0 96 1.8546 0.5476
0.3044 17.0 102 1.7485 0.5
0.3044 18.0 108 1.6544 0.5476
0.2005 19.0 114 1.7817 0.5714
0.0634 20.0 120 2.6836 0.5238
0.0634 21.0 126 2.3476 0.5714
0.0488 22.0 132 2.3551 0.5476
0.0488 23.0 138 2.4123 0.5714
0.0014 24.0 144 2.3855 0.5714
0.0006 25.0 150 2.3709 0.5714
0.0006 26.0 156 2.3623 0.5714
0.0004 27.0 162 2.3621 0.5714
0.0004 28.0 168 2.3646 0.5952
0.0003 29.0 174 2.3639 0.5952
0.0003 30.0 180 2.3665 0.5952
0.0003 31.0 186 2.3692 0.5952
0.0002 32.0 192 2.3723 0.5952
0.0002 33.0 198 2.3750 0.5952
0.0002 34.0 204 2.3777 0.5714
0.0002 35.0 210 2.3806 0.5714
0.0002 36.0 216 2.3834 0.5714
0.0002 37.0 222 2.3855 0.5714
0.0002 38.0 228 2.3872 0.5714
0.0001 39.0 234 2.3885 0.5714
0.0001 40.0 240 2.3895 0.5714
0.0001 41.0 246 2.3902 0.5714
0.0001 42.0 252 2.3903 0.5714
0.0001 43.0 258 2.3903 0.5714
0.0001 44.0 264 2.3903 0.5714
0.0001 45.0 270 2.3903 0.5714
0.0001 46.0 276 2.3903 0.5714
0.0001 47.0 282 2.3903 0.5714
0.0001 48.0 288 2.3903 0.5714
0.0001 49.0 294 2.3903 0.5714
0.0001 50.0 300 2.3903 0.5714

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