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# squarerun2

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4284
- F1 Macro: 0.4676
- F1 Micro: 0.5606
- F1 Weighted: 0.5361
- Precision Macro: 0.4718
- Precision Micro: 0.5606
- Precision Weighted: 0.5334
- Recall Macro: 0.4835
- Recall Micro: 0.5606
- Recall Weighted: 0.5606
- Accuracy: 0.5606

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 20

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9016        | 1.0   | 29   | 1.8764          | 0.1011   | 0.2424   | 0.1401      | 0.0721          | 0.2424          | 0.1001             | 0.1761       | 0.2424       | 0.2424          | 0.2424   |
| 1.8787        | 2.0   | 58   | 1.8750          | 0.0485   | 0.2045   | 0.0695      | 0.0292          | 0.2045          | 0.0418             | 0.1429       | 0.2045       | 0.2045          | 0.2045   |
| 1.9345        | 3.0   | 87   | 1.8624          | 0.0485   | 0.2045   | 0.0695      | 0.0292          | 0.2045          | 0.0418             | 0.1429       | 0.2045       | 0.2045          | 0.2045   |
| 1.6663        | 4.0   | 116  | 1.7239          | 0.2230   | 0.3561   | 0.2738      | 0.3173          | 0.3561          | 0.3549             | 0.2725       | 0.3561       | 0.3561          | 0.3561   |
| 1.3847        | 5.0   | 145  | 1.4880          | 0.3420   | 0.4697   | 0.4038      | 0.4521          | 0.4697          | 0.4846             | 0.3893       | 0.4697       | 0.4697          | 0.4697   |
| 1.6559        | 6.0   | 174  | 1.4056          | 0.3479   | 0.4773   | 0.4108      | 0.3865          | 0.4773          | 0.4276             | 0.3870       | 0.4773       | 0.4773          | 0.4773   |
| 1.335         | 7.0   | 203  | 1.3768          | 0.3875   | 0.5152   | 0.4527      | 0.3933          | 0.5152          | 0.4447             | 0.4265       | 0.5152       | 0.5152          | 0.5152   |
| 1.2514        | 8.0   | 232  | 1.2345          | 0.4536   | 0.5606   | 0.5207      | 0.4701          | 0.5606          | 0.5257             | 0.4766       | 0.5606       | 0.5606          | 0.5606   |
| 0.6979        | 9.0   | 261  | 1.1501          | 0.5305   | 0.6364   | 0.6097      | 0.5491          | 0.6364          | 0.6127             | 0.5391       | 0.6364       | 0.6364          | 0.6364   |
| 1.0417        | 10.0  | 290  | 1.1654          | 0.5206   | 0.6136   | 0.5900      | 0.5215          | 0.6136          | 0.5935             | 0.5464       | 0.6136       | 0.6136          | 0.6136   |
| 0.7314        | 11.0  | 319  | 1.1566          | 0.5376   | 0.6212   | 0.6109      | 0.5387          | 0.6212          | 0.6154             | 0.5514       | 0.6212       | 0.6212          | 0.6212   |
| 0.7902        | 12.0  | 348  | 1.1624          | 0.5397   | 0.6212   | 0.6140      | 0.5422          | 0.6212          | 0.6209             | 0.5505       | 0.6212       | 0.6212          | 0.6212   |
| 0.7503        | 13.0  | 377  | 1.1359          | 0.5377   | 0.6288   | 0.6126      | 0.5472          | 0.6288          | 0.6143             | 0.5455       | 0.6288       | 0.6288          | 0.6288   |
| 0.586         | 14.0  | 406  | 1.1512          | 0.5441   | 0.6288   | 0.6141      | 0.5361          | 0.6288          | 0.6033             | 0.5557       | 0.6288       | 0.6288          | 0.6288   |
| 0.6869        | 15.0  | 435  | 1.1306          | 0.5323   | 0.6288   | 0.6117      | 0.5270          | 0.6288          | 0.6043             | 0.5475       | 0.6288       | 0.6288          | 0.6288   |
| 0.5498        | 16.0  | 464  | 1.1293          | 0.5373   | 0.6288   | 0.6117      | 0.5353          | 0.6288          | 0.6039             | 0.5471       | 0.6288       | 0.6288          | 0.6288   |
| 0.5037        | 17.0  | 493  | 1.1635          | 0.5290   | 0.6212   | 0.6005      | 0.5374          | 0.6212          | 0.6022             | 0.5398       | 0.6212       | 0.6212          | 0.6212   |
| 0.3624        | 18.0  | 522  | 1.0994          | 0.5700   | 0.6591   | 0.6414      | 0.5815          | 0.6591          | 0.6409             | 0.5743       | 0.6591       | 0.6591          | 0.6591   |
| 0.3387        | 19.0  | 551  | 1.0944          | 0.5643   | 0.6515   | 0.6367      | 0.5556          | 0.6515          | 0.6268             | 0.5781       | 0.6515       | 0.6515          | 0.6515   |
| 0.4052        | 20.0  | 580  | 1.0934          | 0.5683   | 0.6591   | 0.6432      | 0.5681          | 0.6591          | 0.6393             | 0.5798       | 0.6591       | 0.6591          | 0.6591   |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

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