finetuned-vietnamese-food

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the indian_vietnam_images dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3760
  • Accuracy: 0.8958

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 4
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.1058 0.0910 100 1.9974 0.5694
1.4012 0.1820 200 1.4076 0.6855
1.3551 0.2730 300 1.1650 0.7264
1.1111 0.3640 400 1.0998 0.7062
1.0038 0.4550 500 0.9087 0.7483
0.9599 0.5460 600 0.8278 0.7682
1.0932 0.6369 700 0.9115 0.7360
0.7807 0.7279 800 0.8011 0.7730
0.8237 0.8189 900 0.8345 0.7726
0.7288 0.9099 1000 0.6427 0.8258
0.7982 1.0009 1100 0.6427 0.8215
0.7331 1.0919 1200 0.6423 0.8183
0.6849 1.1829 1300 0.6820 0.8151
0.671 1.2739 1400 0.6325 0.8191
0.7307 1.3649 1500 0.6079 0.8286
0.7499 1.4559 1600 0.5832 0.8346
0.7004 1.5469 1700 0.6048 0.8342
0.7543 1.6379 1800 0.5612 0.8394
0.5557 1.7288 1900 0.5740 0.8318
0.5019 1.8198 2000 0.5064 0.8561
0.7043 1.9108 2100 0.5513 0.8441
0.519 2.0018 2200 0.5862 0.8350
0.3366 2.0928 2300 0.5159 0.8517
0.4167 2.1838 2400 0.5386 0.8469
0.402 2.2748 2500 0.5614 0.8374
0.4133 2.3658 2600 0.4756 0.8652
0.4751 2.4568 2700 0.4882 0.8612
0.3108 2.5478 2800 0.4946 0.8648
0.3218 2.6388 2900 0.4707 0.8680
0.282 2.7298 3000 0.4407 0.8712
0.2823 2.8207 3100 0.4843 0.8712
0.3498 2.9117 3200 0.4609 0.8744
0.3196 3.0027 3300 0.4369 0.8763
0.2822 3.0937 3400 0.4662 0.8748
0.4166 3.1847 3500 0.4539 0.8779
0.1904 3.2757 3600 0.4205 0.8887
0.388 3.3667 3700 0.4163 0.8863
0.2851 3.4577 3800 0.4168 0.8891
0.2455 3.5487 3900 0.4004 0.8930
0.2804 3.6397 4000 0.4044 0.8938
0.2008 3.7307 4100 0.3833 0.8950
0.2487 3.8217 4200 0.3812 0.8958
0.2077 3.9126 4300 0.3760 0.8958

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.5.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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Evaluation results