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---
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-256-1-2
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: 0.9716684155299056
- name: Recall
type: recall
value: 0.9754098360655737
- name: Precision
type: precision
value: 0.9802306425041186
---
<!-- 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-256-1-2
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: 0.0859
- Accuracy: 0.9717
- F1 Score: 0.9778
- Recall: 0.9754
- Precision: 0.9802
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.6722 | 0.99 | 33 | 0.6499 | 0.6401 | 0.7806 | 1.0 | 0.6401 |
| 0.5431 | 2.0 | 67 | 0.4164 | 0.7817 | 0.8531 | 0.9902 | 0.7494 |
| 0.393 | 2.99 | 100 | 0.2833 | 0.8877 | 0.9078 | 0.8639 | 0.9564 |
| 0.354 | 4.0 | 134 | 0.1930 | 0.9276 | 0.9436 | 0.9459 | 0.9413 |
| 0.3007 | 4.99 | 167 | 0.1585 | 0.9370 | 0.9511 | 0.9557 | 0.9464 |
| 0.2898 | 6.0 | 201 | 0.1445 | 0.9465 | 0.9581 | 0.9557 | 0.9605 |
| 0.2824 | 6.99 | 234 | 0.1353 | 0.9465 | 0.9580 | 0.9525 | 0.9635 |
| 0.2763 | 8.0 | 268 | 0.1359 | 0.9486 | 0.9603 | 0.9721 | 0.9488 |
| 0.2473 | 8.99 | 301 | 0.1213 | 0.9570 | 0.9664 | 0.9672 | 0.9656 |
| 0.2598 | 10.0 | 335 | 0.1091 | 0.9570 | 0.9665 | 0.9705 | 0.9626 |
| 0.2476 | 10.99 | 368 | 0.1041 | 0.9633 | 0.9714 | 0.9754 | 0.9675 |
| 0.2376 | 12.0 | 402 | 0.0997 | 0.9601 | 0.9686 | 0.9623 | 0.9751 |
| 0.2402 | 12.99 | 435 | 0.0972 | 0.9622 | 0.9704 | 0.9672 | 0.9736 |
| 0.2324 | 14.0 | 469 | 0.0950 | 0.9664 | 0.9739 | 0.9803 | 0.9676 |
| 0.2256 | 14.99 | 502 | 0.0909 | 0.9706 | 0.9770 | 0.9754 | 0.9786 |
| 0.21 | 16.0 | 536 | 0.0922 | 0.9622 | 0.9703 | 0.9656 | 0.9752 |
| 0.217 | 16.99 | 569 | 0.0933 | 0.9612 | 0.9695 | 0.9656 | 0.9736 |
| 0.2092 | 18.0 | 603 | 0.0891 | 0.9664 | 0.9738 | 0.9754 | 0.9722 |
| 0.2063 | 18.99 | 636 | 0.0913 | 0.9654 | 0.9730 | 0.9738 | 0.9722 |
| 0.2217 | 20.0 | 670 | 0.0917 | 0.9643 | 0.9720 | 0.9672 | 0.9768 |
| 0.1952 | 20.99 | 703 | 0.0859 | 0.9717 | 0.9778 | 0.9754 | 0.9802 |
| 0.2068 | 22.0 | 737 | 0.0907 | 0.9685 | 0.9755 | 0.9770 | 0.9739 |
| 0.1914 | 22.99 | 770 | 0.0847 | 0.9696 | 0.9763 | 0.9787 | 0.9739 |
| 0.1961 | 24.0 | 804 | 0.0870 | 0.9685 | 0.9755 | 0.9770 | 0.9739 |
| 0.1911 | 24.99 | 837 | 0.0884 | 0.9664 | 0.9739 | 0.9770 | 0.9707 |
| 0.1961 | 26.0 | 871 | 0.0870 | 0.9685 | 0.9754 | 0.9738 | 0.9770 |
| 0.1978 | 26.99 | 904 | 0.0871 | 0.9685 | 0.9754 | 0.9754 | 0.9754 |
| 0.1854 | 28.0 | 938 | 0.0858 | 0.9685 | 0.9755 | 0.9770 | 0.9739 |
| 0.1733 | 28.99 | 971 | 0.0860 | 0.9685 | 0.9754 | 0.9738 | 0.9770 |
| 0.1762 | 29.55 | 990 | 0.0858 | 0.9664 | 0.9738 | 0.9738 | 0.9738 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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