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vit-base-VietnameseFood

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metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-VietnameseFood
    results: []

vit-base-VietnameseFood

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a Vietnamese Food dataset (https://huggingface.co/datasets/TuyenTrungLe/vietnamese_food_images) with More than 17k images were on the train set, 2k5 were on the validation set, and 5k were on the test set.

image/png

It achieves the following results on the evaluation set:

  • Loss: 1.2489
  • Accuracy: 0.8925

Although the loss is quite high, the model predicted well with test set with 0.8639 accuracy and a loss of 0.4871

image/png

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: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 6
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.4936 0.1818 100 1.5493 0.6901
0.848 0.3636 200 0.9488 0.7851
0.6619 0.5455 300 0.8240 0.7865
0.6868 0.7273 400 0.6671 0.8298
0.6127 0.9091 500 0.6296 0.8296
0.4413 1.0909 600 0.6003 0.8339
0.3484 1.2727 700 0.6349 0.8153
0.3529 1.4545 800 0.5235 0.8581
0.4104 1.6364 900 0.5407 0.8512
0.3097 1.8182 1000 0.5537 0.8423
0.2527 2.0 1100 0.4871 0.8639
0.1571 2.1818 1200 0.5507 0.8587
0.2164 2.3636 1300 0.5598 0.8585
0.1875 2.5455 1400 0.5787 0.8522
0.1314 2.7273 1500 0.5262 0.8643
0.1671 2.9091 1600 0.5686 0.8587
0.0807 3.0909 1700 0.5912 0.8633
0.0989 3.2727 1800 0.6392 0.8679
0.0586 3.4545 1900 0.6587 0.8651
0.0672 3.6364 2000 0.6542 0.8758
0.0342 3.8182 2100 0.6533 0.8786
0.0484 4.0 2200 0.7314 0.8756
0.0678 4.1818 2300 0.8517 0.8788
0.075 4.3636 2400 0.9576 0.8843
0.0201 4.5455 2500 1.0758 0.8845
0.1238 4.7273 2600 1.1375 0.8871
0.0434 4.9091 2700 1.2226 0.8877
0.0493 5.0909 2800 1.1938 0.8923
0.0055 5.2727 2900 1.2594 0.8903
0.0039 5.4545 3000 1.2709 0.8887
0.0445 5.6364 3100 1.2420 0.8921
0.0347 5.8182 3200 1.2609 0.8915
0.0657 6.0 3300 1.2489 0.8925

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.19.1