File size: 4,883 Bytes
e47ed66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
---
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
|