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# square_run_first_vote_full_pic_75

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: 1.7586
- F1 Macro: 0.4234
- F1 Micro: 0.5152
- F1 Weighted: 0.4789
- Precision Macro: 0.4562
- Precision Micro: 0.5152
- Precision Weighted: 0.5061
- Recall Macro: 0.4488
- Recall Micro: 0.5152
- Recall Weighted: 0.5152
- Accuracy: 0.5152

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9675        | 1.0   | 58   | 1.9322          | 0.1051   | 0.1894   | 0.1204      | 0.1047          | 0.1894          | 0.1191             | 0.1661       | 0.1894       | 0.1894          | 0.1894   |
| 1.8921        | 2.0   | 116  | 1.9534          | 0.0786   | 0.1818   | 0.0855      | 0.0551          | 0.1818          | 0.0597             | 0.1656       | 0.1818       | 0.1818          | 0.1818   |
| 1.9081        | 3.0   | 174  | 1.8370          | 0.1526   | 0.2803   | 0.1976      | 0.1283          | 0.2803          | 0.1642             | 0.2117       | 0.2803       | 0.2803          | 0.2803   |
| 1.5193        | 4.0   | 232  | 1.7240          | 0.1963   | 0.3258   | 0.2445      | 0.2948          | 0.3258          | 0.3289             | 0.2476       | 0.3258       | 0.3258          | 0.3258   |
| 1.7743        | 5.0   | 290  | 1.5478          | 0.3382   | 0.4318   | 0.3920      | 0.3494          | 0.4318          | 0.4204             | 0.3837       | 0.4318       | 0.4318          | 0.4318   |
| 1.9879        | 6.0   | 348  | 1.5070          | 0.3157   | 0.4470   | 0.3865      | 0.4597          | 0.4470          | 0.5200             | 0.3499       | 0.4470       | 0.4470          | 0.4470   |
| 1.9096        | 7.0   | 406  | 1.4281          | 0.3859   | 0.4545   | 0.4410      | 0.4248          | 0.4545          | 0.4763             | 0.3931       | 0.4545       | 0.4545          | 0.4545   |
| 1.4577        | 8.0   | 464  | 1.4558          | 0.3862   | 0.4773   | 0.4381      | 0.3827          | 0.4773          | 0.4425             | 0.4346       | 0.4773       | 0.4773          | 0.4773   |
| 1.9664        | 9.0   | 522  | 1.5863          | 0.3757   | 0.4773   | 0.4227      | 0.3967          | 0.4773          | 0.4530             | 0.4288       | 0.4773       | 0.4773          | 0.4773   |
| 0.7655        | 10.0  | 580  | 1.3785          | 0.4015   | 0.5      | 0.4621      | 0.5175          | 0.5             | 0.5866             | 0.4427       | 0.5          | 0.5             | 0.5      |
| 0.707         | 11.0  | 638  | 1.3441          | 0.4772   | 0.5530   | 0.5356      | 0.4915          | 0.5530          | 0.5453             | 0.4861       | 0.5530       | 0.5530          | 0.5530   |
| 0.782         | 12.0  | 696  | 1.3983          | 0.4716   | 0.5530   | 0.5325      | 0.4860          | 0.5530          | 0.5432             | 0.4877       | 0.5530       | 0.5530          | 0.5530   |
| 0.7316        | 13.0  | 754  | 1.6155          | 0.4880   | 0.5530   | 0.5497      | 0.5085          | 0.5530          | 0.5892             | 0.5080       | 0.5530       | 0.5530          | 0.5530   |
| 1.0819        | 14.0  | 812  | 1.4869          | 0.4936   | 0.5379   | 0.5312      | 0.5124          | 0.5379          | 0.5370             | 0.4900       | 0.5379       | 0.5379          | 0.5379   |
| 0.8757        | 15.0  | 870  | 1.6936          | 0.4741   | 0.5303   | 0.5300      | 0.4809          | 0.5303          | 0.5481             | 0.4847       | 0.5303       | 0.5303          | 0.5303   |
| 0.7228        | 16.0  | 928  | 1.7370          | 0.4442   | 0.5227   | 0.4986      | 0.4401          | 0.5227          | 0.4939             | 0.4646       | 0.5227       | 0.5227          | 0.5227   |
| 0.3016        | 17.0  | 986  | 1.6977          | 0.5279   | 0.5682   | 0.5642      | 0.6353          | 0.5682          | 0.5994             | 0.5176       | 0.5682       | 0.5682          | 0.5682   |
| 0.2097        | 18.0  | 1044 | 1.9026          | 0.4769   | 0.5606   | 0.5414      | 0.5384          | 0.5606          | 0.5783             | 0.4819       | 0.5606       | 0.5606          | 0.5606   |
| 0.0388        | 19.0  | 1102 | 1.8276          | 0.5259   | 0.6136   | 0.5981      | 0.5252          | 0.6136          | 0.5945             | 0.5382       | 0.6136       | 0.6136          | 0.6136   |
| 0.4837        | 20.0  | 1160 | 1.8658          | 0.5336   | 0.5985   | 0.5863      | 0.5502          | 0.5985          | 0.5866             | 0.5342       | 0.5985       | 0.5985          | 0.5985   |
| 0.1531        | 21.0  | 1218 | 2.0415          | 0.4703   | 0.5606   | 0.5384      | 0.4917          | 0.5606          | 0.5489             | 0.4762       | 0.5606       | 0.5606          | 0.5606   |
| 0.0142        | 22.0  | 1276 | 2.0812          | 0.4969   | 0.5303   | 0.5260      | 0.5067          | 0.5303          | 0.5364             | 0.5008       | 0.5303       | 0.5303          | 0.5303   |
| 0.0036        | 23.0  | 1334 | 2.0662          | 0.5315   | 0.5758   | 0.5781      | 0.5480          | 0.5758          | 0.5925             | 0.5316       | 0.5758       | 0.5758          | 0.5758   |
| 0.0065        | 24.0  | 1392 | 2.1023          | 0.5090   | 0.5606   | 0.5516      | 0.5140          | 0.5606          | 0.5550             | 0.5154       | 0.5606       | 0.5606          | 0.5606   |
| 0.1359        | 25.0  | 1450 | 2.0555          | 0.4994   | 0.5455   | 0.5440      | 0.5018          | 0.5455          | 0.5474             | 0.5021       | 0.5455       | 0.5455          | 0.5455   |
| 0.0037        | 26.0  | 1508 | 2.1745          | 0.5206   | 0.5758   | 0.5691      | 0.5289          | 0.5758          | 0.5695             | 0.5204       | 0.5758       | 0.5758          | 0.5758   |
| 0.0391        | 27.0  | 1566 | 2.2087          | 0.5204   | 0.5758   | 0.5676      | 0.5335          | 0.5758          | 0.5745             | 0.5228       | 0.5758       | 0.5758          | 0.5758   |
| 0.0017        | 28.0  | 1624 | 2.1219          | 0.5178   | 0.5682   | 0.5633      | 0.5218          | 0.5682          | 0.5649             | 0.5212       | 0.5682       | 0.5682          | 0.5682   |
| 0.0015        | 29.0  | 1682 | 2.1455          | 0.5198   | 0.5682   | 0.5618      | 0.5342          | 0.5682          | 0.5641             | 0.5190       | 0.5682       | 0.5682          | 0.5682   |
| 0.0015        | 30.0  | 1740 | 2.1308          | 0.5192   | 0.5682   | 0.5617      | 0.5315          | 0.5682          | 0.5621             | 0.5190       | 0.5682       | 0.5682          | 0.5682   |


### Framework versions

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

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