vit-base-patch16-224-Trial006_007_008-YEL_STEM3

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0514
  • Accuracy: 1.0

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: 5e-05
  • train_batch_size: 60
  • eval_batch_size: 60
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 240
  • 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
0.7488 0.8 3 0.7067 0.5253
0.6693 1.87 7 0.6195 0.6263
0.5816 2.93 11 0.5114 0.7273
0.4669 4.0 15 0.3586 0.8990
0.3998 4.8 18 0.2867 0.9192
0.3579 5.87 22 0.2326 0.9596
0.3087 6.93 26 0.1684 0.9596
0.2448 8.0 30 0.1525 0.9697
0.207 8.8 33 0.1863 0.9394
0.2283 9.87 37 0.1259 0.9697
0.2354 10.93 41 0.1090 0.9495
0.2452 12.0 45 0.0999 0.9596
0.2373 12.8 48 0.0702 0.9899
0.1516 13.87 52 0.0749 0.9697
0.1893 14.93 56 0.0808 0.9798
0.1761 16.0 60 0.0514 1.0
0.1971 16.8 63 0.0954 0.9798
0.1801 17.87 67 0.0536 0.9899
0.2004 18.93 71 0.0836 0.9596
0.1899 20.0 75 0.0500 0.9899
0.2222 20.8 78 0.0874 0.9798
0.1341 21.87 82 0.0371 1.0
0.2187 22.93 86 0.0545 1.0
0.1547 24.0 90 0.0445 1.0
0.1307 24.8 93 0.0404 1.0
0.1563 25.87 97 0.0377 1.0
0.1467 26.93 101 0.0405 1.0
0.1413 28.0 105 0.0379 1.0
0.1317 28.8 108 0.0367 1.0
0.1264 29.87 112 0.0359 1.0
0.128 30.93 116 0.0328 1.0
0.1292 32.0 120 0.0356 1.0
0.1549 32.8 123 0.0339 1.0
0.1272 33.87 127 0.0296 1.0
0.1376 34.93 131 0.0295 1.0
0.1884 36.0 135 0.0308 1.0
0.1536 36.8 138 0.0295 1.0
0.166 37.87 142 0.0299 1.0
0.0929 38.93 146 0.0307 1.0
0.1592 40.0 150 0.0307 1.0

Framework versions

  • Transformers 4.30.0.dev0
  • Pytorch 1.12.1
  • Datasets 2.12.0
  • Tokenizers 0.13.1
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Evaluation results