|
--- |
|
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 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# teacher-status-van-tiny-256-0 |
|
|
|
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co./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 |
|
|
