
<!-- 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. -->

# square_run_second_vote

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: 1.1557
- F1 Macro: 0.5777
- F1 Micro: 0.6667
- F1 Weighted: 0.6629
- Precision Macro: 0.5756
- Precision Micro: 0.6667
- Precision Weighted: 0.6734
- Recall Macro: 0.5912
- Recall Micro: 0.6667
- Recall Weighted: 0.6667
- Accuracy: 0.6667

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.8754        | 1.0   | 58   | 1.7961          | 0.1385   | 0.2803   | 0.1722      | 0.1426          | 0.2803          | 0.1603             | 0.2098       | 0.2803       | 0.2803          | 0.2803   |
| 2.0246        | 2.0   | 116  | 2.0138          | 0.2236   | 0.3106   | 0.2484      | 0.2558          | 0.3106          | 0.2692             | 0.2842       | 0.3106       | 0.3106          | 0.3106   |
| 1.6189        | 3.0   | 174  | 1.5039          | 0.2444   | 0.3864   | 0.3195      | 0.2633          | 0.3864          | 0.3301             | 0.2847       | 0.3864       | 0.3864          | 0.3864   |
| 1.3445        | 4.0   | 232  | 1.3982          | 0.3287   | 0.4394   | 0.3866      | 0.3186          | 0.4394          | 0.3730             | 0.3696       | 0.4394       | 0.4394          | 0.4394   |
| 1.3387        | 5.0   | 290  | 1.1920          | 0.4401   | 0.5758   | 0.5265      | 0.4315          | 0.5758          | 0.5031             | 0.4683       | 0.5758       | 0.5758          | 0.5758   |
| 1.1664        | 6.0   | 348  | 1.1778          | 0.4179   | 0.5076   | 0.4988      | 0.5068          | 0.5076          | 0.5862             | 0.4395       | 0.5076       | 0.5076          | 0.5076   |
| 1.1622        | 7.0   | 406  | 1.1723          | 0.4518   | 0.5379   | 0.5251      | 0.4514          | 0.5379          | 0.5526             | 0.4867       | 0.5379       | 0.5379          | 0.5379   |
| 0.9827        | 8.0   | 464  | 1.0619          | 0.5084   | 0.6212   | 0.6074      | 0.5037          | 0.6212          | 0.6140             | 0.5345       | 0.6212       | 0.6212          | 0.6212   |
| 1.3416        | 9.0   | 522  | 1.3995          | 0.3997   | 0.5      | 0.4690      | 0.4218          | 0.5             | 0.5024             | 0.4509       | 0.5          | 0.5             | 0.5      |
| 0.758         | 10.0  | 580  | 1.1693          | 0.5066   | 0.5985   | 0.5836      | 0.5262          | 0.5985          | 0.6031             | 0.5279       | 0.5985       | 0.5985          | 0.5985   |
| 0.7758        | 11.0  | 638  | 1.0800          | 0.5491   | 0.6515   | 0.6320      | 0.5729          | 0.6515          | 0.6501             | 0.5710       | 0.6515       | 0.6515          | 0.6515   |
| 0.2319        | 12.0  | 696  | 1.1553          | 0.5467   | 0.6742   | 0.6410      | 0.5816          | 0.6742          | 0.6699             | 0.5711       | 0.6742       | 0.6742          | 0.6742   |
| 0.3528        | 13.0  | 754  | 1.1685          | 0.5794   | 0.6894   | 0.6711      | 0.5887          | 0.6894          | 0.6752             | 0.5955       | 0.6894       | 0.6894          | 0.6894   |
| 0.6238        | 14.0  | 812  | 1.1781          | 0.5579   | 0.6439   | 0.6285      | 0.5451          | 0.6439          | 0.6278             | 0.5856       | 0.6439       | 0.6439          | 0.6439   |
| 0.1869        | 15.0  | 870  | 1.2305          | 0.5146   | 0.6061   | 0.5983      | 0.5032          | 0.6061          | 0.6013             | 0.5369       | 0.6061       | 0.6061          | 0.6061   |
| 0.1015        | 16.0  | 928  | 1.3576          | 0.5019   | 0.5909   | 0.5932      | 0.5440          | 0.5909          | 0.6312             | 0.4959       | 0.5909       | 0.5909          | 0.5909   |
| 0.3809        | 17.0  | 986  | 1.2998          | 0.5667   | 0.6591   | 0.6527      | 0.5828          | 0.6591          | 0.6885             | 0.5838       | 0.6591       | 0.6591          | 0.6591   |
| 0.0887        | 18.0  | 1044 | 1.4154          | 0.5572   | 0.6667   | 0.6489      | 0.5682          | 0.6667          | 0.6518             | 0.5683       | 0.6667       | 0.6667          | 0.6667   |
| 0.1422        | 19.0  | 1102 | 1.3989          | 0.5609   | 0.6667   | 0.6472      | 0.5672          | 0.6667          | 0.6420             | 0.5695       | 0.6667       | 0.6667          | 0.6667   |
| 0.0037        | 20.0  | 1160 | 1.5134          | 0.5242   | 0.6212   | 0.6078      | 0.5263          | 0.6212          | 0.6093             | 0.5374       | 0.6212       | 0.6212          | 0.6212   |
| 0.0602        | 21.0  | 1218 | 1.5349          | 0.5660   | 0.6667   | 0.6544      | 0.5710          | 0.6667          | 0.6503             | 0.5671       | 0.6667       | 0.6667          | 0.6667   |
| 0.0353        | 22.0  | 1276 | 1.4489          | 0.6137   | 0.7045   | 0.6919      | 0.6146          | 0.7045          | 0.6909             | 0.6242       | 0.7045       | 0.7045          | 0.7045   |
| 0.001         | 23.0  | 1334 | 1.4781          | 0.5715   | 0.6667   | 0.6541      | 0.5657          | 0.6667          | 0.6449             | 0.5805       | 0.6667       | 0.6667          | 0.6667   |
| 0.0007        | 24.0  | 1392 | 1.6326          | 0.5713   | 0.6591   | 0.6511      | 0.5871          | 0.6591          | 0.6648             | 0.5786       | 0.6591       | 0.6591          | 0.6591   |
| 0.0084        | 25.0  | 1450 | 1.5856          | 0.5684   | 0.6591   | 0.6569      | 0.5662          | 0.6591          | 0.6672             | 0.5802       | 0.6591       | 0.6591          | 0.6591   |
| 0.0008        | 26.0  | 1508 | 1.5799          | 0.5826   | 0.6818   | 0.6675      | 0.5849          | 0.6818          | 0.6632             | 0.5884       | 0.6818       | 0.6818          | 0.6818   |
| 0.0053        | 27.0  | 1566 | 1.5308          | 0.5719   | 0.6667   | 0.6556      | 0.5667          | 0.6667          | 0.6524             | 0.5843       | 0.6667       | 0.6667          | 0.6667   |
| 0.0004        | 28.0  | 1624 | 1.5639          | 0.5732   | 0.6667   | 0.6617      | 0.5684          | 0.6667          | 0.6673             | 0.5867       | 0.6667       | 0.6667          | 0.6667   |
| 0.0007        | 29.0  | 1682 | 1.5346          | 0.5835   | 0.6742   | 0.6678      | 0.5786          | 0.6742          | 0.6703             | 0.5965       | 0.6742       | 0.6742          | 0.6742   |
| 0.0004        | 30.0  | 1740 | 1.5232          | 0.5791   | 0.6742   | 0.6661      | 0.5707          | 0.6742          | 0.6628             | 0.5918       | 0.6742       | 0.6742          | 0.6742   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

- 0 1 2 3 4 5