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# square_run_second_vote_full_pic_age_gender

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0942
- F1 Macro: 0.4946
- F1 Micro: 0.5909
- F1 Weighted: 0.5567
- Precision Macro: 0.5352
- Precision Micro: 0.5909
- Precision Weighted: 0.6194
- Recall Macro: 0.5362
- Recall Micro: 0.5909
- Recall Weighted: 0.5909
- Accuracy: 0.5909

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | Precision Macro | Precision Micro | Precision Weighted | Recall Macro | Recall Micro | Recall Weighted | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.7962        | 1.0   | 58   | 1.7841          | 0.2169   | 0.3409   | 0.2791      | 0.2076          | 0.3409          | 0.2573             | 0.2540       | 0.3409       | 0.3409          | 0.3409   |
| 1.7195        | 2.0   | 116  | 1.8915          | 0.2134   | 0.3409   | 0.2372      | 0.1677          | 0.3409          | 0.1872             | 0.3116       | 0.3409       | 0.3409          | 0.3409   |
| 1.4056        | 3.0   | 174  | 1.5382          | 0.3310   | 0.4470   | 0.3785      | 0.4080          | 0.4470          | 0.4904             | 0.3773       | 0.4470       | 0.4470          | 0.4470   |
| 1.3472        | 4.0   | 232  | 1.3224          | 0.4005   | 0.5530   | 0.4824      | 0.4535          | 0.5530          | 0.5284             | 0.4615       | 0.5530       | 0.5530          | 0.5530   |
| 1.3266        | 5.0   | 290  | 1.3801          | 0.3815   | 0.5      | 0.4467      | 0.3655          | 0.5             | 0.4261             | 0.4203       | 0.5          | 0.5             | 0.5      |
| 1.3514        | 6.0   | 348  | 1.4009          | 0.3982   | 0.4697   | 0.4703      | 0.5142          | 0.4697          | 0.6060             | 0.4123       | 0.4697       | 0.4697          | 0.4697   |
| 1.0789        | 7.0   | 406  | 1.0679          | 0.5153   | 0.6136   | 0.5974      | 0.5624          | 0.6136          | 0.6350             | 0.5174       | 0.6136       | 0.6136          | 0.6136   |
| 0.9054        | 8.0   | 464  | 1.0248          | 0.5610   | 0.6591   | 0.6392      | 0.5675          | 0.6591          | 0.6528             | 0.5790       | 0.6591       | 0.6591          | 0.6591   |
| 0.9475        | 9.0   | 522  | 1.0533          | 0.5533   | 0.6439   | 0.6313      | 0.5630          | 0.6439          | 0.6377             | 0.5627       | 0.6439       | 0.6439          | 0.6439   |
| 0.7595        | 10.0  | 580  | 1.2404          | 0.5064   | 0.6061   | 0.5985      | 0.5585          | 0.6061          | 0.6540             | 0.5220       | 0.6061       | 0.6061          | 0.6061   |
| 0.6635        | 11.0  | 638  | 1.2577          | 0.5481   | 0.6515   | 0.6358      | 0.5697          | 0.6515          | 0.6554             | 0.5644       | 0.6515       | 0.6515          | 0.6515   |
| 0.6638        | 12.0  | 696  | 1.1971          | 0.5943   | 0.6894   | 0.6847      | 0.6031          | 0.6894          | 0.6966             | 0.6035       | 0.6894       | 0.6894          | 0.6894   |
| 1.3747        | 13.0  | 754  | 1.3014          | 0.5376   | 0.6136   | 0.6094      | 0.5734          | 0.6136          | 0.6522             | 0.5461       | 0.6136       | 0.6136          | 0.6136   |
| 0.3888        | 14.0  | 812  | 1.3645          | 0.5671   | 0.6364   | 0.6212      | 0.6146          | 0.6364          | 0.6701             | 0.5834       | 0.6364       | 0.6364          | 0.6364   |
| 0.3119        | 15.0  | 870  | 1.4637          | 0.5753   | 0.6591   | 0.6482      | 0.5955          | 0.6591          | 0.6694             | 0.5839       | 0.6591       | 0.6591          | 0.6591   |
| 0.1874        | 16.0  | 928  | 1.4016          | 0.5387   | 0.6288   | 0.6220      | 0.5523          | 0.6288          | 0.6300             | 0.5383       | 0.6288       | 0.6288          | 0.6288   |
| 0.0585        | 17.0  | 986  | 1.5412          | 0.5895   | 0.6894   | 0.6834      | 0.6173          | 0.6894          | 0.7082             | 0.5938       | 0.6894       | 0.6894          | 0.6894   |
| 0.0425        | 18.0  | 1044 | 1.5022          | 0.6341   | 0.6970   | 0.6980      | 0.6602          | 0.6970          | 0.7305             | 0.6383       | 0.6970       | 0.6970          | 0.6970   |
| 0.1893        | 19.0  | 1102 | 1.5766          | 0.6294   | 0.6818   | 0.6736      | 0.6630          | 0.6818          | 0.6847             | 0.6225       | 0.6818       | 0.6818          | 0.6818   |
| 0.0059        | 20.0  | 1160 | 1.5288          | 0.6187   | 0.7273   | 0.7173      | 0.6260          | 0.7273          | 0.7246             | 0.6302       | 0.7273       | 0.7273          | 0.7273   |
| 0.0019        | 21.0  | 1218 | 1.5794          | 0.6116   | 0.7121   | 0.7044      | 0.6149          | 0.7121          | 0.7040             | 0.6158       | 0.7121       | 0.7121          | 0.7121   |
| 0.0043        | 22.0  | 1276 | 1.6290          | 0.5979   | 0.6970   | 0.6910      | 0.6144          | 0.6970          | 0.6984             | 0.5944       | 0.6970       | 0.6970          | 0.6970   |
| 0.0012        | 23.0  | 1334 | 1.6983          | 0.6387   | 0.6894   | 0.6835      | 0.6647          | 0.6894          | 0.6874             | 0.6310       | 0.6894       | 0.6894          | 0.6894   |
| 0.0007        | 24.0  | 1392 | 1.6381          | 0.6084   | 0.6970   | 0.6986      | 0.6195          | 0.6970          | 0.7039             | 0.6007       | 0.6970       | 0.6970          | 0.6970   |
| 0.0035        | 25.0  | 1450 | 1.6691          | 0.6100   | 0.6970   | 0.6975      | 0.6162          | 0.6970          | 0.7018             | 0.6077       | 0.6970       | 0.6970          | 0.6970   |
| 0.0318        | 26.0  | 1508 | 1.6443          | 0.6116   | 0.7045   | 0.7036      | 0.6223          | 0.7045          | 0.7080             | 0.6055       | 0.7045       | 0.7045          | 0.7045   |
| 0.0005        | 27.0  | 1566 | 1.6647          | 0.6203   | 0.7121   | 0.7117      | 0.6312          | 0.7121          | 0.7167             | 0.6139       | 0.7121       | 0.7121          | 0.7121   |
| 0.0059        | 28.0  | 1624 | 1.6387          | 0.6243   | 0.7273   | 0.7198      | 0.6269          | 0.7273          | 0.7211             | 0.6309       | 0.7273       | 0.7273          | 0.7273   |
| 0.0009        | 29.0  | 1682 | 1.6511          | 0.5960   | 0.6894   | 0.6864      | 0.5988          | 0.6894          | 0.6864             | 0.5963       | 0.6894       | 0.6894          | 0.6894   |
| 0.003         | 30.0  | 1740 | 1.6608          | 0.6046   | 0.6970   | 0.6945      | 0.6080          | 0.6970          | 0.6954             | 0.6047       | 0.6970       | 0.6970          | 0.6970   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0

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