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.
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
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
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Base model
google/vit-base-patch16-224-in21k