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hushem_1x_deit_tiny_rms_lr001_fold3

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: 1.7042
  • Accuracy: 0.2791

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 3.1061 0.2326
4.0184 2.0 12 1.7666 0.2558
4.0184 3.0 18 1.6279 0.2558
1.7385 4.0 24 1.9636 0.2558
1.583 5.0 30 1.6503 0.2558
1.583 6.0 36 1.4630 0.2326
1.4859 7.0 42 3.2936 0.2326
1.4859 8.0 48 2.0073 0.2558
2.0303 9.0 54 1.4859 0.2326
1.4062 10.0 60 1.6529 0.2326
1.4062 11.0 66 1.4259 0.2791
1.359 12.0 72 1.3892 0.2558
1.359 13.0 78 1.4650 0.3023
1.3464 14.0 84 1.4368 0.2558
1.262 15.0 90 1.4241 0.2558
1.262 16.0 96 1.6562 0.3023
1.2521 17.0 102 1.3729 0.3023
1.2521 18.0 108 1.5241 0.2093
1.2212 19.0 114 1.5032 0.3023
1.1882 20.0 120 1.4178 0.2558
1.1882 21.0 126 1.8156 0.3023
1.1382 22.0 132 1.5280 0.2558
1.1382 23.0 138 1.5037 0.2326
1.0802 24.0 144 1.5058 0.3488
1.1083 25.0 150 1.5421 0.2791
1.1083 26.0 156 1.5398 0.2558
1.0555 27.0 162 1.8560 0.2791
1.0555 28.0 168 1.9193 0.2558
1.0051 29.0 174 1.5934 0.3256
0.958 30.0 180 1.6481 0.2791
0.958 31.0 186 1.5950 0.2791
0.9855 32.0 192 1.5539 0.2558
0.9855 33.0 198 1.6644 0.2791
0.9482 34.0 204 1.6743 0.2326
0.9401 35.0 210 1.6352 0.3023
0.9401 36.0 216 1.6896 0.2791
0.9225 37.0 222 1.7369 0.2326
0.9225 38.0 228 1.6916 0.2558
0.8891 39.0 234 1.6919 0.2791
0.8732 40.0 240 1.7104 0.2791
0.8732 41.0 246 1.7028 0.2791
0.8715 42.0 252 1.7042 0.2791
0.8715 43.0 258 1.7042 0.2791
0.8826 44.0 264 1.7042 0.2791
0.8986 45.0 270 1.7042 0.2791
0.8986 46.0 276 1.7042 0.2791
0.8589 47.0 282 1.7042 0.2791
0.8589 48.0 288 1.7042 0.2791
0.9236 49.0 294 1.7042 0.2791
0.8539 50.0 300 1.7042 0.2791

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