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# square_run_first_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: 1.7554
- F1 Macro: 0.3377
- F1 Micro: 0.4091
- F1 Weighted: 0.3702
- Precision Macro: 0.3432
- Precision Micro: 0.4091
- Precision Weighted: 0.3813
- Recall Macro: 0.3764
- Recall Micro: 0.4091
- Recall Weighted: 0.4091
- Accuracy: 0.4091

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9621        | 1.0   | 58   | 1.9294          | 0.1188   | 0.2121   | 0.1307      | 0.0924          | 0.2121          | 0.1040             | 0.2009       | 0.2121       | 0.2121          | 0.2121   |
| 1.6897        | 2.0   | 116  | 1.8039          | 0.2212   | 0.3258   | 0.2698      | 0.2607          | 0.3258          | 0.3321             | 0.2817       | 0.3258       | 0.3258          | 0.3258   |
| 1.9018        | 3.0   | 174  | 1.8333          | 0.1488   | 0.2727   | 0.1810      | 0.1261          | 0.2727          | 0.1499             | 0.2142       | 0.2727       | 0.2727          | 0.2727   |
| 1.3206        | 4.0   | 232  | 1.8180          | 0.1941   | 0.2955   | 0.2237      | 0.2405          | 0.2955          | 0.2639             | 0.2493       | 0.2955       | 0.2955          | 0.2955   |
| 1.7794        | 5.0   | 290  | 1.4852          | 0.3258   | 0.4167   | 0.3866      | 0.3195          | 0.4167          | 0.3831             | 0.3576       | 0.4167       | 0.4167          | 0.4167   |
| 1.7676        | 6.0   | 348  | 1.4509          | 0.4341   | 0.4924   | 0.4845      | 0.4298          | 0.4924          | 0.4816             | 0.4437       | 0.4924       | 0.4924          | 0.4924   |
| 1.6212        | 7.0   | 406  | 1.5437          | 0.4281   | 0.4773   | 0.4424      | 0.4381          | 0.4773          | 0.4653             | 0.4668       | 0.4773       | 0.4773          | 0.4773   |
| 1.5547        | 8.0   | 464  | 1.4854          | 0.3596   | 0.4242   | 0.4034      | 0.3810          | 0.4242          | 0.4362             | 0.3863       | 0.4242       | 0.4242          | 0.4242   |
| 1.5353        | 9.0   | 522  | 1.3877          | 0.4190   | 0.5076   | 0.4726      | 0.4442          | 0.5076          | 0.5046             | 0.4533       | 0.5076       | 0.5076          | 0.5076   |
| 0.7126        | 10.0  | 580  | 1.4099          | 0.4492   | 0.5227   | 0.5096      | 0.4685          | 0.5227          | 0.5359             | 0.4685       | 0.5227       | 0.5227          | 0.5227   |
| 1.0466        | 11.0  | 638  | 1.5217          | 0.4805   | 0.5682   | 0.5464      | 0.4933          | 0.5682          | 0.5566             | 0.4972       | 0.5682       | 0.5682          | 0.5682   |
| 0.648         | 12.0  | 696  | 1.5465          | 0.4666   | 0.5303   | 0.5284      | 0.4870          | 0.5303          | 0.5655             | 0.4872       | 0.5303       | 0.5303          | 0.5303   |
| 0.6292        | 13.0  | 754  | 1.5671          | 0.4553   | 0.5152   | 0.5116      | 0.5059          | 0.5152          | 0.5740             | 0.4652       | 0.5152       | 0.5152          | 0.5152   |
| 0.3081        | 14.0  | 812  | 1.5835          | 0.5087   | 0.5909   | 0.5700      | 0.5276          | 0.5909          | 0.5752             | 0.5130       | 0.5909       | 0.5909          | 0.5909   |
| 0.349         | 15.0  | 870  | 1.7548          | 0.4364   | 0.5076   | 0.4959      | 0.4563          | 0.5076          | 0.5064             | 0.4397       | 0.5076       | 0.5076          | 0.5076   |
| 0.2594        | 16.0  | 928  | 1.9070          | 0.4717   | 0.5455   | 0.5287      | 0.4803          | 0.5455          | 0.5289             | 0.4780       | 0.5455       | 0.5455          | 0.5455   |
| 0.2384        | 17.0  | 986  | 1.8439          | 0.5212   | 0.5909   | 0.5797      | 0.5462          | 0.5909          | 0.5900             | 0.5211       | 0.5909       | 0.5909          | 0.5909   |
| 0.1308        | 18.0  | 1044 | 2.0280          | 0.5064   | 0.5758   | 0.5659      | 0.5375          | 0.5758          | 0.5885             | 0.5074       | 0.5758       | 0.5758          | 0.5758   |
| 0.042         | 19.0  | 1102 | 1.9038          | 0.5345   | 0.6136   | 0.6004      | 0.5672          | 0.6136          | 0.6168             | 0.5360       | 0.6136       | 0.6136          | 0.6136   |
| 0.0605        | 20.0  | 1160 | 2.1862          | 0.5215   | 0.5909   | 0.5855      | 0.5231          | 0.5909          | 0.5867             | 0.5264       | 0.5909       | 0.5909          | 0.5909   |
| 0.0153        | 21.0  | 1218 | 2.1651          | 0.5037   | 0.5682   | 0.5637      | 0.5110          | 0.5682          | 0.5718             | 0.5059       | 0.5682       | 0.5682          | 0.5682   |
| 0.0069        | 22.0  | 1276 | 2.1574          | 0.4779   | 0.5455   | 0.5377      | 0.4819          | 0.5455          | 0.5367             | 0.4793       | 0.5455       | 0.5455          | 0.5455   |
| 0.0138        | 23.0  | 1334 | 2.3532          | 0.4938   | 0.5606   | 0.5544      | 0.4944          | 0.5606          | 0.5692             | 0.5119       | 0.5606       | 0.5606          | 0.5606   |
| 0.0016        | 24.0  | 1392 | 2.3575          | 0.4795   | 0.5606   | 0.5442      | 0.4724          | 0.5606          | 0.5342             | 0.4922       | 0.5606       | 0.5606          | 0.5606   |
| 0.2167        | 25.0  | 1450 | 2.4082          | 0.5045   | 0.5758   | 0.5691      | 0.5056          | 0.5758          | 0.5715             | 0.5127       | 0.5758       | 0.5758          | 0.5758   |
| 0.0024        | 26.0  | 1508 | 2.4224          | 0.5014   | 0.5682   | 0.5612      | 0.5003          | 0.5682          | 0.5623             | 0.5102       | 0.5682       | 0.5682          | 0.5682   |
| 0.0042        | 27.0  | 1566 | 2.3936          | 0.5133   | 0.5833   | 0.5745      | 0.5123          | 0.5833          | 0.5716             | 0.5189       | 0.5833       | 0.5833          | 0.5833   |
| 0.0008        | 28.0  | 1624 | 2.3829          | 0.5239   | 0.5985   | 0.5885      | 0.5309          | 0.5985          | 0.5892             | 0.5290       | 0.5985       | 0.5985          | 0.5985   |
| 0.0009        | 29.0  | 1682 | 2.4101          | 0.4891   | 0.5606   | 0.5532      | 0.4833          | 0.5606          | 0.5484             | 0.4976       | 0.5606       | 0.5606          | 0.5606   |
| 0.0009        | 30.0  | 1740 | 2.4118          | 0.5015   | 0.5758   | 0.5656      | 0.4970          | 0.5758          | 0.5602             | 0.5108       | 0.5758       | 0.5758          | 0.5758   |


### Framework versions

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

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