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# squarerun_large_model

This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5150
- F1 Macro: 0.4837
- F1 Micro: 0.5909
- F1 Weighted: 0.5569
- Precision Macro: 0.5183
- Precision Micro: 0.5909
- Precision Weighted: 0.5764
- Recall Macro: 0.5013
- Recall Micro: 0.5909
- Recall Weighted: 0.5909
- Accuracy: 0.5909

## 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: 25

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.917         | 1.0   | 29   | 1.9115          | 0.1066   | 0.2197   | 0.1273      | 0.0780          | 0.2197          | 0.0923             | 0.1832       | 0.2197       | 0.2197          | 0.2197   |
| 1.6762        | 2.0   | 58   | 1.6722          | 0.2733   | 0.3561   | 0.3005      | 0.3141          | 0.3561          | 0.3684             | 0.3355       | 0.3561       | 0.3561          | 0.3561   |
| 1.9664        | 3.0   | 87   | 1.5057          | 0.3554   | 0.4545   | 0.4060      | 0.3734          | 0.4545          | 0.4129             | 0.3857       | 0.4545       | 0.4545          | 0.4545   |
| 1.1934        | 4.0   | 116  | 1.4217          | 0.3130   | 0.4091   | 0.3530      | 0.3414          | 0.4091          | 0.3818             | 0.3629       | 0.4091       | 0.4091          | 0.4091   |
| 1.0968        | 5.0   | 145  | 1.1879          | 0.4608   | 0.5758   | 0.5258      | 0.4807          | 0.5758          | 0.5438             | 0.5045       | 0.5758       | 0.5758          | 0.5758   |
| 1.1313        | 6.0   | 174  | 1.2307          | 0.4964   | 0.5530   | 0.5243      | 0.5850          | 0.5530          | 0.6114             | 0.5196       | 0.5530       | 0.5530          | 0.5530   |
| 1.0807        | 7.0   | 203  | 1.2771          | 0.4088   | 0.5303   | 0.4772      | 0.5393          | 0.5303          | 0.5816             | 0.4304       | 0.5303       | 0.5303          | 0.5303   |
| 1.1825        | 8.0   | 232  | 1.2339          | 0.4528   | 0.5682   | 0.5175      | 0.5544          | 0.5682          | 0.6169             | 0.4920       | 0.5682       | 0.5682          | 0.5682   |
| 0.4454        | 9.0   | 261  | 1.0474          | 0.6064   | 0.6970   | 0.6763      | 0.6334          | 0.6970          | 0.6868             | 0.6100       | 0.6970       | 0.6970          | 0.6970   |
| 0.5439        | 10.0  | 290  | 1.6815          | 0.4580   | 0.5152   | 0.4920      | 0.5394          | 0.5152          | 0.5951             | 0.4903       | 0.5152       | 0.5152          | 0.5152   |
| 0.4256        | 11.0  | 319  | 1.1378          | 0.5800   | 0.6667   | 0.6495      | 0.5801          | 0.6667          | 0.6435             | 0.5907       | 0.6667       | 0.6667          | 0.6667   |
| 0.4968        | 12.0  | 348  | 1.4229          | 0.5307   | 0.6136   | 0.6013      | 0.5348          | 0.6136          | 0.6095             | 0.5486       | 0.6136       | 0.6136          | 0.6136   |
| 0.3408        | 13.0  | 377  | 1.4445          | 0.5426   | 0.6288   | 0.6095      | 0.5559          | 0.6288          | 0.6307             | 0.5621       | 0.6288       | 0.6288          | 0.6288   |
| 0.2914        | 14.0  | 406  | 1.4277          | 0.6009   | 0.6515   | 0.6470      | 0.7068          | 0.6515          | 0.6868             | 0.5958       | 0.6515       | 0.6515          | 0.6515   |
| 0.2003        | 15.0  | 435  | 1.5517          | 0.5770   | 0.6288   | 0.6296      | 0.5890          | 0.6288          | 0.6475             | 0.5792       | 0.6288       | 0.6288          | 0.6288   |
| 0.0871        | 16.0  | 464  | 1.4812          | 0.5702   | 0.6515   | 0.6407      | 0.5777          | 0.6515          | 0.6491             | 0.5785       | 0.6515       | 0.6515          | 0.6515   |
| 0.0352        | 17.0  | 493  | 2.1052          | 0.5007   | 0.5985   | 0.5744      | 0.5466          | 0.5985          | 0.6130             | 0.5127       | 0.5985       | 0.5985          | 0.5985   |
| 0.0101        | 18.0  | 522  | 1.9978          | 0.5725   | 0.6212   | 0.6223      | 0.6152          | 0.6212          | 0.6559             | 0.5672       | 0.6212       | 0.6212          | 0.6212   |
| 0.0035        | 19.0  | 551  | 2.0304          | 0.5880   | 0.6439   | 0.6388      | 0.6698          | 0.6439          | 0.6936             | 0.5805       | 0.6439       | 0.6439          | 0.6439   |
| 0.0013        | 20.0  | 580  | 2.1374          | 0.5514   | 0.6364   | 0.6224      | 0.6025          | 0.6364          | 0.6765             | 0.5685       | 0.6364       | 0.6364          | 0.6364   |
| 0.0589        | 21.0  | 609  | 1.7676          | 0.5879   | 0.6439   | 0.6396      | 0.5940          | 0.6439          | 0.6407             | 0.5889       | 0.6439       | 0.6439          | 0.6439   |
| 0.0263        | 22.0  | 638  | 1.8416          | 0.5785   | 0.6439   | 0.6327      | 0.6016          | 0.6439          | 0.6454             | 0.5758       | 0.6439       | 0.6439          | 0.6439   |
| 0.0028        | 23.0  | 667  | 1.9843          | 0.6068   | 0.6667   | 0.6569      | 0.6631          | 0.6667          | 0.6882             | 0.6069       | 0.6667       | 0.6667          | 0.6667   |
| 0.0006        | 24.0  | 696  | 1.9432          | 0.6157   | 0.6742   | 0.6655      | 0.6603          | 0.6742          | 0.6853             | 0.6152       | 0.6742       | 0.6742          | 0.6742   |
| 0.0004        | 25.0  | 725  | 1.9346          | 0.6089   | 0.6667   | 0.6569      | 0.6548          | 0.6667          | 0.6763             | 0.6073       | 0.6667       | 0.6667          | 0.6667   |


### Framework versions

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

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