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hushem_1x_deit_base_sgd_001_fold5

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: 1.3053
  • Accuracy: 0.3902

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: 0.001
  • 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 1.3696 0.1951
1.4452 2.0 12 1.3651 0.1951
1.4452 3.0 18 1.3609 0.1951
1.4223 4.0 24 1.3571 0.2439
1.4169 5.0 30 1.3535 0.2683
1.4169 6.0 36 1.3500 0.2683
1.3958 7.0 42 1.3468 0.2927
1.3958 8.0 48 1.3440 0.2927
1.4015 9.0 54 1.3412 0.3171
1.3839 10.0 60 1.3385 0.3171
1.3839 11.0 66 1.3363 0.3171
1.3741 12.0 72 1.3340 0.3171
1.3741 13.0 78 1.3317 0.3171
1.3638 14.0 84 1.3297 0.3171
1.3638 15.0 90 1.3277 0.3415
1.3638 16.0 96 1.3263 0.3415
1.3489 17.0 102 1.3246 0.3659
1.3489 18.0 108 1.3230 0.3659
1.3469 19.0 114 1.3214 0.3902
1.3356 20.0 120 1.3200 0.3902
1.3356 21.0 126 1.3187 0.3902
1.3412 22.0 132 1.3174 0.3902
1.3412 23.0 138 1.3162 0.3902
1.3294 24.0 144 1.3151 0.3902
1.3266 25.0 150 1.3139 0.3659
1.3266 26.0 156 1.3128 0.3659
1.3206 27.0 162 1.3119 0.3659
1.3206 28.0 168 1.3110 0.3659
1.3159 29.0 174 1.3102 0.3659
1.325 30.0 180 1.3096 0.3659
1.325 31.0 186 1.3089 0.3902
1.3129 32.0 192 1.3082 0.3902
1.3129 33.0 198 1.3077 0.3902
1.3071 34.0 204 1.3071 0.3902
1.315 35.0 210 1.3067 0.3902
1.315 36.0 216 1.3063 0.3902
1.3109 37.0 222 1.3060 0.3902
1.3109 38.0 228 1.3057 0.3902
1.2941 39.0 234 1.3055 0.3902
1.3144 40.0 240 1.3054 0.3902
1.3144 41.0 246 1.3053 0.3902
1.3008 42.0 252 1.3053 0.3902
1.3008 43.0 258 1.3053 0.3902
1.3092 44.0 264 1.3053 0.3902
1.297 45.0 270 1.3053 0.3902
1.297 46.0 276 1.3053 0.3902
1.2978 47.0 282 1.3053 0.3902
1.2978 48.0 288 1.3053 0.3902
1.3087 49.0 294 1.3053 0.3902
1.3046 50.0 300 1.3053 0.3902

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