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vit-base-25ep

This model is a fine-tuned version of google/vit-base-patch16-224 on the vuongnhathien/30VNFoods dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5506
  • Accuracy: 0.8486

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: 0.0003
  • train_batch_size: 64
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6167 1.0 275 0.5712 0.8354
0.3183 2.0 550 0.5564 0.8406
0.1729 3.0 825 0.5955 0.8433
0.139 4.0 1100 0.6453 0.8406
0.0775 5.0 1375 0.6044 0.8517
0.0784 6.0 1650 0.7265 0.8414
0.0502 7.0 1925 0.6977 0.8533
0.0525 8.0 2200 0.7100 0.8549
0.0311 9.0 2475 0.7423 0.8525
0.026 10.0 2750 0.7901 0.8461
0.0183 11.0 3025 0.7261 0.8592
0.0218 12.0 3300 0.8014 0.8485
0.0135 13.0 3575 0.7391 0.8584
0.0066 14.0 3850 0.6938 0.8740
0.0047 15.0 4125 0.6765 0.8815
0.0052 16.0 4400 0.6611 0.8839
0.0033 17.0 4675 0.6794 0.8803
0.0037 18.0 4950 0.6724 0.8811
0.0026 19.0 5225 0.6759 0.8875
0.0031 20.0 5500 0.6699 0.8855
0.0028 21.0 5775 0.6720 0.8847
0.0029 22.0 6050 0.6746 0.8843
0.0016 23.0 6325 0.6731 0.8859
0.0016 24.0 6600 0.6759 0.8859
0.0019 25.0 6875 0.6767 0.8847

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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85.8M params
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F32
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Finetuned from

Evaluation results