Nguyen Tien
Model save
e47ed66
metadata
license: apache-2.0
base_model: Visual-Attention-Network/van-tiny
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
  - precision
model-index:
  - name: teacher-status-van-tiny-256-0
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9777777777777777
          - name: Recall
            type: recall
            value: 0.9893162393162394
          - name: Precision
            type: precision
            value: 0.9788583509513742

teacher-status-van-tiny-256-0

This model is a fine-tuned version of Visual-Attention-Network/van-tiny on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0672
  • Accuracy: 0.9778
  • F1 Score: 0.9841
  • Recall: 0.9893
  • Precision: 0.9789

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Score Recall Precision
0.6788 0.99 47 0.6437 0.6933 0.8189 1.0 0.6933
0.463 2.0 95 0.3406 0.8756 0.9162 0.9808 0.8596
0.3596 2.99 142 0.2072 0.9304 0.9504 0.9615 0.9395
0.3505 4.0 190 0.1564 0.9526 0.9661 0.9744 0.9580
0.2962 4.99 237 0.1262 0.9556 0.9681 0.9722 0.9640
0.2762 6.0 285 0.1038 0.9644 0.9745 0.9808 0.9684
0.2604 6.99 332 0.0932 0.9719 0.9798 0.9829 0.9766
0.2427 8.0 380 0.0928 0.9719 0.9797 0.9786 0.9807
0.2465 8.99 427 0.0898 0.9719 0.9797 0.9786 0.9807
0.2519 10.0 475 0.0913 0.9689 0.9775 0.9765 0.9786
0.2258 10.99 522 0.0847 0.9733 0.9809 0.9872 0.9747
0.2184 12.0 570 0.0812 0.9793 0.9851 0.9893 0.9809
0.2208 12.99 617 0.0693 0.9807 0.9861 0.9872 0.9851
0.2201 14.0 665 0.0628 0.9763 0.9829 0.9850 0.9809
0.2251 14.99 712 0.0811 0.9733 0.9810 0.9915 0.9707
0.2135 16.0 760 0.0718 0.9763 0.9829 0.9850 0.9809
0.1851 16.99 807 0.0791 0.9763 0.9830 0.9872 0.9788
0.2152 18.0 855 0.0737 0.9748 0.9818 0.9808 0.9829
0.1871 18.99 902 0.0814 0.9763 0.9830 0.9872 0.9788
0.1714 20.0 950 0.0692 0.9763 0.9830 0.9893 0.9768
0.188 20.99 997 0.0641 0.9778 0.9840 0.9850 0.9829
0.191 22.0 1045 0.0644 0.9793 0.9851 0.9872 0.9830
0.2025 22.99 1092 0.0675 0.9793 0.9850 0.9829 0.9871
0.1753 24.0 1140 0.0655 0.9822 0.9872 0.9893 0.9851
0.1857 24.99 1187 0.0731 0.9793 0.9851 0.9915 0.9789
0.2007 26.0 1235 0.0677 0.9793 0.9851 0.9915 0.9789
0.2086 26.99 1282 0.0640 0.9793 0.9851 0.9893 0.9809
0.1666 28.0 1330 0.0712 0.9778 0.9841 0.9893 0.9789
0.157 28.99 1377 0.0661 0.9807 0.9862 0.9893 0.9830
0.1758 29.68 1410 0.0672 0.9778 0.9841 0.9893 0.9789

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.0
  • Tokenizers 0.15.0