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# square_run_second_vote_full_pic_75_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: 1.3432
- F1 Macro: 0.3328
- F1 Micro: 0.4697
- F1 Weighted: 0.4098
- Precision Macro: 0.3270
- Precision Micro: 0.4697
- Precision Weighted: 0.4161
- Recall Macro: 0.3849
- Recall Micro: 0.4697
- Recall Weighted: 0.4697
- Accuracy: 0.4697

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.874         | 1.0   | 58   | 1.8367          | 0.1624   | 0.2576   | 0.2032      | 0.1524          | 0.2576          | 0.1870             | 0.2024       | 0.2576       | 0.2576          | 0.2576   |
| 1.9776        | 2.0   | 116  | 1.9333          | 0.0771   | 0.1439   | 0.0881      | 0.0743          | 0.1439          | 0.0940             | 0.1402       | 0.1439       | 0.1439          | 0.1439   |
| 1.645         | 3.0   | 174  | 1.8058          | 0.1674   | 0.2803   | 0.1932      | 0.1766          | 0.2803          | 0.1880             | 0.2217       | 0.2803       | 0.2803          | 0.2803   |
| 1.6353        | 4.0   | 232  | 1.6974          | 0.2498   | 0.3636   | 0.2906      | 0.2124          | 0.3636          | 0.2464             | 0.3095       | 0.3636       | 0.3636          | 0.3636   |
| 1.6165        | 5.0   | 290  | 1.6144          | 0.2440   | 0.3409   | 0.2804      | 0.2940          | 0.3409          | 0.3290             | 0.2857       | 0.3409       | 0.3409          | 0.3409   |
| 1.7496        | 6.0   | 348  | 1.7440          | 0.2610   | 0.3636   | 0.3308      | 0.2845          | 0.3636          | 0.3528             | 0.2888       | 0.3636       | 0.3636          | 0.3636   |
| 1.8783        | 7.0   | 406  | 1.5126          | 0.3535   | 0.4167   | 0.3994      | 0.3637          | 0.4167          | 0.4048             | 0.3646       | 0.4167       | 0.4167          | 0.4167   |
| 1.2903        | 8.0   | 464  | 1.5240          | 0.3589   | 0.4167   | 0.4036      | 0.3702          | 0.4167          | 0.4107             | 0.3644       | 0.4167       | 0.4167          | 0.4167   |
| 1.8885        | 9.0   | 522  | 1.5423          | 0.3727   | 0.4470   | 0.4283      | 0.3800          | 0.4470          | 0.4329             | 0.3856       | 0.4470       | 0.4470          | 0.4470   |
| 1.0726        | 10.0  | 580  | 1.8002          | 0.3168   | 0.4015   | 0.3651      | 0.3364          | 0.4015          | 0.3788             | 0.3456       | 0.4015       | 0.4015          | 0.4015   |
| 1.2297        | 11.0  | 638  | 1.9532          | 0.3087   | 0.3788   | 0.3653      | 0.3752          | 0.3788          | 0.4335             | 0.3300       | 0.3788       | 0.3788          | 0.3788   |
| 0.7152        | 12.0  | 696  | 1.8452          | 0.3120   | 0.3864   | 0.3677      | 0.3922          | 0.3864          | 0.4313             | 0.3156       | 0.3864       | 0.3864          | 0.3864   |
| 0.7479        | 13.0  | 754  | 1.7619          | 0.3686   | 0.4394   | 0.4348      | 0.3981          | 0.4394          | 0.4546             | 0.3645       | 0.4394       | 0.4394          | 0.4394   |
| 0.2766        | 14.0  | 812  | 1.8000          | 0.3931   | 0.4924   | 0.4657      | 0.4146          | 0.4924          | 0.4792             | 0.4080       | 0.4924       | 0.4924          | 0.4924   |
| 0.4092        | 15.0  | 870  | 2.0428          | 0.3611   | 0.4318   | 0.4252      | 0.3772          | 0.4318          | 0.4421             | 0.3673       | 0.4318       | 0.4318          | 0.4318   |
| 0.1272        | 16.0  | 928  | 2.1450          | 0.3493   | 0.4242   | 0.4203      | 0.3651          | 0.4242          | 0.4419             | 0.3598       | 0.4242       | 0.4242          | 0.4242   |
| 0.2751        | 17.0  | 986  | 2.3002          | 0.3782   | 0.4394   | 0.4357      | 0.4548          | 0.4394          | 0.5101             | 0.3712       | 0.4394       | 0.4394          | 0.4394   |
| 0.3277        | 18.0  | 1044 | 2.2109          | 0.3832   | 0.4470   | 0.4450      | 0.4073          | 0.4470          | 0.4770             | 0.3856       | 0.4470       | 0.4470          | 0.4470   |
| 0.0134        | 19.0  | 1102 | 2.4450          | 0.3585   | 0.4470   | 0.4219      | 0.3987          | 0.4470          | 0.4533             | 0.3729       | 0.4470       | 0.4470          | 0.4470   |
| 0.0737        | 20.0  | 1160 | 2.5434          | 0.3468   | 0.4091   | 0.4054      | 0.3581          | 0.4091          | 0.4161             | 0.3508       | 0.4091       | 0.4091          | 0.4091   |
| 0.0203        | 21.0  | 1218 | 2.8118          | 0.3895   | 0.4773   | 0.4493      | 0.4176          | 0.4773          | 0.4699             | 0.4098       | 0.4773       | 0.4773          | 0.4773   |
| 0.0072        | 22.0  | 1276 | 2.7996          | 0.3620   | 0.4242   | 0.4165      | 0.3783          | 0.4242          | 0.4359             | 0.3729       | 0.4242       | 0.4242          | 0.4242   |
| 0.1251        | 23.0  | 1334 | 2.9001          | 0.4009   | 0.4394   | 0.4291      | 0.4500          | 0.4394          | 0.4703             | 0.4067       | 0.4394       | 0.4394          | 0.4394   |
| 0.0054        | 24.0  | 1392 | 2.8660          | 0.4011   | 0.4470   | 0.4327      | 0.4245          | 0.4470          | 0.4535             | 0.4147       | 0.4470       | 0.4470          | 0.4470   |
| 0.0091        | 25.0  | 1450 | 2.8868          | 0.3852   | 0.4167   | 0.4086      | 0.3965          | 0.4167          | 0.4115             | 0.3858       | 0.4167       | 0.4167          | 0.4167   |
| 0.002         | 26.0  | 1508 | 2.9311          | 0.3952   | 0.4394   | 0.4272      | 0.4054          | 0.4394          | 0.4343             | 0.4043       | 0.4394       | 0.4394          | 0.4394   |
| 0.0008        | 27.0  | 1566 | 2.9526          | 0.4052   | 0.4470   | 0.4388      | 0.4173          | 0.4470          | 0.4483             | 0.4118       | 0.4470       | 0.4470          | 0.4470   |
| 0.002         | 28.0  | 1624 | 3.0159          | 0.4074   | 0.4470   | 0.4389      | 0.4227          | 0.4470          | 0.4489             | 0.4116       | 0.4470       | 0.4470          | 0.4470   |
| 0.0017        | 29.0  | 1682 | 2.9797          | 0.4121   | 0.4545   | 0.4431      | 0.4192          | 0.4545          | 0.4464             | 0.4193       | 0.4545       | 0.4545          | 0.4545   |
| 0.0016        | 30.0  | 1740 | 2.9981          | 0.3677   | 0.4394   | 0.4256      | 0.3741          | 0.4394          | 0.4271             | 0.3761       | 0.4394       | 0.4394          | 0.4394   |


### Framework versions

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

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