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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
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: 0.9831460674157303
- name: Recall
type: recall
value: 0.9789473684210527
- name: Precision
type: precision
value: 0.9893617021276596
---
<!-- 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
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: 0.0988
- Accuracy: 0.9831
- F1 Score: 0.9841
- Recall: 0.9789
- Precision: 0.9894
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.6928 | 0.96 | 12 | 0.6904 | 0.6685 | 0.7631 | 1.0 | 0.6169 |
| 0.6893 | 2.0 | 25 | 0.6683 | 0.5393 | 0.6985 | 1.0 | 0.5367 |
| 0.6726 | 2.96 | 37 | 0.5704 | 0.5843 | 0.7197 | 1.0 | 0.5621 |
| 0.5295 | 4.0 | 50 | 0.4148 | 0.9213 | 0.9263 | 0.9263 | 0.9263 |
| 0.4745 | 4.96 | 62 | 0.3108 | 0.9382 | 0.9430 | 0.9579 | 0.9286 |
| 0.4206 | 6.0 | 75 | 0.2301 | 0.9438 | 0.9474 | 0.9474 | 0.9474 |
| 0.3898 | 6.96 | 87 | 0.1820 | 0.9494 | 0.9519 | 0.9368 | 0.9674 |
| 0.3153 | 8.0 | 100 | 0.1545 | 0.9494 | 0.9538 | 0.9789 | 0.93 |
| 0.3077 | 8.96 | 112 | 0.1521 | 0.9607 | 0.9622 | 0.9368 | 0.9889 |
| 0.3048 | 10.0 | 125 | 0.1331 | 0.9607 | 0.9626 | 0.9474 | 0.9783 |
| 0.3004 | 10.96 | 137 | 0.1314 | 0.9607 | 0.9634 | 0.9684 | 0.9583 |
| 0.2839 | 12.0 | 150 | 0.1272 | 0.9607 | 0.9622 | 0.9368 | 0.9889 |
| 0.286 | 12.96 | 162 | 0.1189 | 0.9607 | 0.9622 | 0.9368 | 0.9889 |
| 0.2473 | 14.0 | 175 | 0.0977 | 0.9719 | 0.9733 | 0.9579 | 0.9891 |
| 0.2774 | 14.96 | 187 | 0.0988 | 0.9831 | 0.9841 | 0.9789 | 0.9894 |
| 0.2541 | 16.0 | 200 | 0.0969 | 0.9719 | 0.9733 | 0.9579 | 0.9891 |
| 0.2383 | 16.96 | 212 | 0.1042 | 0.9719 | 0.9733 | 0.9579 | 0.9891 |
| 0.2552 | 18.0 | 225 | 0.1081 | 0.9719 | 0.9733 | 0.9579 | 0.9891 |
| 0.2223 | 18.96 | 237 | 0.1150 | 0.9663 | 0.9681 | 0.9579 | 0.9785 |
| 0.2561 | 20.0 | 250 | 0.1234 | 0.9551 | 0.9574 | 0.9474 | 0.9677 |
| 0.2462 | 20.96 | 262 | 0.1178 | 0.9607 | 0.9630 | 0.9579 | 0.9681 |
| 0.2294 | 22.0 | 275 | 0.1262 | 0.9382 | 0.9430 | 0.9579 | 0.9286 |
| 0.2296 | 22.96 | 287 | 0.1290 | 0.9438 | 0.9479 | 0.9579 | 0.9381 |
| 0.2224 | 24.0 | 300 | 0.1153 | 0.9494 | 0.9529 | 0.9579 | 0.9479 |
| 0.2205 | 24.96 | 312 | 0.1150 | 0.9494 | 0.9529 | 0.9579 | 0.9479 |
| 0.2169 | 26.0 | 325 | 0.1121 | 0.9551 | 0.9574 | 0.9474 | 0.9677 |
| 0.2212 | 26.96 | 337 | 0.1145 | 0.9494 | 0.9529 | 0.9579 | 0.9479 |
| 0.2188 | 28.0 | 350 | 0.1131 | 0.9494 | 0.9524 | 0.9474 | 0.9574 |
| 0.2015 | 28.8 | 360 | 0.1130 | 0.9494 | 0.9524 | 0.9474 | 0.9574 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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