hushem_5x_deit_tiny_sgd_00001_fold5

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: 1.7062
  • Accuracy: 0.2439

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
1.53 1.0 28 1.7652 0.2439
1.4658 2.0 56 1.7625 0.2439
1.4749 3.0 84 1.7598 0.2439
1.4869 4.0 112 1.7572 0.2439
1.4859 5.0 140 1.7548 0.2439
1.5155 6.0 168 1.7523 0.2439
1.4632 7.0 196 1.7499 0.2439
1.4958 8.0 224 1.7475 0.2439
1.538 9.0 252 1.7452 0.2439
1.5008 10.0 280 1.7432 0.2439
1.4793 11.0 308 1.7411 0.2439
1.483 12.0 336 1.7391 0.2439
1.4966 13.0 364 1.7374 0.2439
1.5231 14.0 392 1.7355 0.2439
1.5038 15.0 420 1.7337 0.2439
1.4896 16.0 448 1.7319 0.2439
1.5043 17.0 476 1.7303 0.2439
1.4967 18.0 504 1.7286 0.2439
1.5162 19.0 532 1.7269 0.2439
1.5126 20.0 560 1.7254 0.2439
1.4809 21.0 588 1.7239 0.2439
1.4877 22.0 616 1.7225 0.2439
1.5048 23.0 644 1.7212 0.2439
1.4932 24.0 672 1.7199 0.2439
1.4898 25.0 700 1.7187 0.2439
1.4408 26.0 728 1.7176 0.2439
1.5027 27.0 756 1.7165 0.2439
1.4716 28.0 784 1.7154 0.2439
1.5167 29.0 812 1.7145 0.2439
1.4795 30.0 840 1.7136 0.2439
1.5126 31.0 868 1.7127 0.2439
1.4908 32.0 896 1.7119 0.2439
1.4785 33.0 924 1.7111 0.2439
1.4672 34.0 952 1.7104 0.2439
1.4938 35.0 980 1.7097 0.2439
1.4756 36.0 1008 1.7092 0.2439
1.4385 37.0 1036 1.7087 0.2439
1.5268 38.0 1064 1.7082 0.2439
1.4939 39.0 1092 1.7078 0.2439
1.4888 40.0 1120 1.7074 0.2439
1.4584 41.0 1148 1.7071 0.2439
1.5033 42.0 1176 1.7068 0.2439
1.5098 43.0 1204 1.7066 0.2439
1.485 44.0 1232 1.7064 0.2439
1.4705 45.0 1260 1.7063 0.2439
1.4946 46.0 1288 1.7062 0.2439
1.4654 47.0 1316 1.7062 0.2439
1.5055 48.0 1344 1.7062 0.2439
1.4868 49.0 1372 1.7062 0.2439
1.489 50.0 1400 1.7062 0.2439

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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Evaluation results