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# square_run_second_vote_full_pic_stratified

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: 0.8527
- F1 Macro: 0.6046
- F1 Micro: 0.7121
- F1 Weighted: 0.6979
- Precision Macro: 0.6148
- Precision Micro: 0.7121
- Precision Weighted: 0.7042
- Recall Macro: 0.6157
- Recall Micro: 0.7121
- Recall Weighted: 0.7121
- Accuracy: 0.7121

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.8233        | 1.0   | 58   | 1.8311          | 0.1942   | 0.2727   | 0.2274      | 0.1804          | 0.2727          | 0.2139             | 0.2367       | 0.2727       | 0.2727          | 0.2727   |
| 1.8698        | 2.0   | 116  | 1.9889          | 0.0840   | 0.1742   | 0.0989      | 0.0796          | 0.1742          | 0.1011             | 0.1719       | 0.1742       | 0.1742          | 0.1742   |
| 1.9152        | 3.0   | 174  | 1.7868          | 0.1549   | 0.2727   | 0.1775      | 0.3078          | 0.2727          | 0.3022             | 0.2068       | 0.2727       | 0.2727          | 0.2727   |
| 1.6147        | 4.0   | 232  | 1.9341          | 0.2055   | 0.2955   | 0.2523      | 0.3212          | 0.2955          | 0.3791             | 0.2645       | 0.2955       | 0.2955          | 0.2955   |
| 1.6414        | 5.0   | 290  | 1.4736          | 0.3384   | 0.4470   | 0.3905      | 0.3487          | 0.4470          | 0.3844             | 0.3667       | 0.4470       | 0.4470          | 0.4470   |
| 1.1299        | 6.0   | 348  | 1.2117          | 0.4394   | 0.5227   | 0.5100      | 0.4779          | 0.5227          | 0.5389             | 0.4409       | 0.5227       | 0.5227          | 0.5227   |
| 1.493         | 7.0   | 406  | 1.3023          | 0.4501   | 0.5227   | 0.5130      | 0.4975          | 0.5227          | 0.5811             | 0.4699       | 0.5227       | 0.5227          | 0.5227   |
| 1.191         | 8.0   | 464  | 1.1375          | 0.5136   | 0.6136   | 0.5937      | 0.5140          | 0.6136          | 0.5852             | 0.5227       | 0.6136       | 0.6136          | 0.6136   |
| 1.6657        | 9.0   | 522  | 0.9972          | 0.5421   | 0.6439   | 0.6344      | 0.5400          | 0.6439          | 0.6329             | 0.5526       | 0.6439       | 0.6439          | 0.6439   |
| 0.6272        | 10.0  | 580  | 1.1733          | 0.4743   | 0.5833   | 0.5586      | 0.4990          | 0.5833          | 0.5887             | 0.4983       | 0.5833       | 0.5833          | 0.5833   |
| 0.3887        | 11.0  | 638  | 1.2098          | 0.4849   | 0.5833   | 0.5713      | 0.4956          | 0.5833          | 0.5931             | 0.5094       | 0.5833       | 0.5833          | 0.5833   |
| 0.5232        | 12.0  | 696  | 1.1906          | 0.5205   | 0.6212   | 0.6061      | 0.5470          | 0.6212          | 0.6370             | 0.5378       | 0.6212       | 0.6212          | 0.6212   |
| 1.1531        | 13.0  | 754  | 1.1958          | 0.5960   | 0.6439   | 0.6364      | 0.6959          | 0.6439          | 0.6619             | 0.5765       | 0.6439       | 0.6439          | 0.6439   |
| 0.4566        | 14.0  | 812  | 1.2707          | 0.5381   | 0.6061   | 0.5919      | 0.5768          | 0.6061          | 0.6055             | 0.5356       | 0.6061       | 0.6061          | 0.6061   |
| 0.6865        | 15.0  | 870  | 1.3936          | 0.5478   | 0.6364   | 0.6304      | 0.5668          | 0.6364          | 0.6634             | 0.5658       | 0.6364       | 0.6364          | 0.6364   |
| 0.4421        | 16.0  | 928  | 1.3080          | 0.5732   | 0.6591   | 0.6616      | 0.5855          | 0.6591          | 0.6781             | 0.5718       | 0.6591       | 0.6591          | 0.6591   |
| 0.3093        | 17.0  | 986  | 1.7662          | 0.4753   | 0.5682   | 0.5524      | 0.5212          | 0.5682          | 0.6010             | 0.4840       | 0.5682       | 0.5682          | 0.5682   |
| 0.134         | 18.0  | 1044 | 1.4813          | 0.5427   | 0.6364   | 0.6250      | 0.5700          | 0.6364          | 0.6476             | 0.5510       | 0.6364       | 0.6364          | 0.6364   |
| 0.0406        | 19.0  | 1102 | 1.5180          | 0.5583   | 0.6515   | 0.6354      | 0.5572          | 0.6515          | 0.6338             | 0.5736       | 0.6515       | 0.6515          | 0.6515   |
| 0.0198        | 20.0  | 1160 | 1.8253          | 0.5403   | 0.6212   | 0.6226      | 0.5579          | 0.6212          | 0.6497             | 0.5487       | 0.6212       | 0.6212          | 0.6212   |
| 0.0054        | 21.0  | 1218 | 1.6285          | 0.5574   | 0.6515   | 0.6478      | 0.5722          | 0.6515          | 0.6595             | 0.5596       | 0.6515       | 0.6515          | 0.6515   |
| 0.2816        | 22.0  | 1276 | 1.7743          | 0.5174   | 0.6212   | 0.6028      | 0.5400          | 0.6212          | 0.6203             | 0.5257       | 0.6212       | 0.6212          | 0.6212   |
| 0.0076        | 23.0  | 1334 | 1.7558          | 0.5353   | 0.6212   | 0.6174      | 0.5506          | 0.6212          | 0.6324             | 0.5389       | 0.6212       | 0.6212          | 0.6212   |
| 0.0046        | 24.0  | 1392 | 1.7770          | 0.5518   | 0.6364   | 0.6346      | 0.5720          | 0.6364          | 0.6581             | 0.5580       | 0.6364       | 0.6364          | 0.6364   |
| 0.0018        | 25.0  | 1450 | 1.5917          | 0.5926   | 0.6818   | 0.6812      | 0.6030          | 0.6818          | 0.6864             | 0.5869       | 0.6818       | 0.6818          | 0.6818   |
| 0.0041        | 26.0  | 1508 | 1.7247          | 0.5866   | 0.6667   | 0.6725      | 0.6022          | 0.6667          | 0.6874             | 0.5788       | 0.6667       | 0.6667          | 0.6667   |
| 0.0013        | 27.0  | 1566 | 1.6674          | 0.5977   | 0.6894   | 0.6852      | 0.6010          | 0.6894          | 0.6861             | 0.5997       | 0.6894       | 0.6894          | 0.6894   |
| 0.0022        | 28.0  | 1624 | 1.7056          | 0.5938   | 0.6818   | 0.6794      | 0.5964          | 0.6818          | 0.6828             | 0.5975       | 0.6818       | 0.6818          | 0.6818   |
| 0.001         | 29.0  | 1682 | 1.6834          | 0.5978   | 0.6894   | 0.6849      | 0.5975          | 0.6894          | 0.6828             | 0.6006       | 0.6894       | 0.6894          | 0.6894   |
| 0.0006        | 30.0  | 1740 | 1.6730          | 0.6075   | 0.6970   | 0.6932      | 0.6149          | 0.6970          | 0.6968             | 0.6081       | 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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