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# square_run_with_16_batch_size

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: 1.4457
- F1 Macro: 0.4685
- F1 Micro: 0.5455
- F1 Weighted: 0.5242
- Precision Macro: 0.5341
- Precision Micro: 0.5455
- Precision Weighted: 0.5870
- Recall Macro: 0.4829
- Recall Micro: 0.5455
- Recall Weighted: 0.5455
- Accuracy: 0.5455

## 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: 16
- 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: 35

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9976        | 1.0   | 29   | 1.9107          | 0.0915   | 0.2045   | 0.1209      | 0.0793          | 0.2045          | 0.1019             | 0.1535       | 0.2045       | 0.2045          | 0.2045   |
| 1.7575        | 2.0   | 58   | 1.8877          | 0.1474   | 0.2348   | 0.1805      | 0.1704          | 0.2348          | 0.2163             | 0.1989       | 0.2348       | 0.2348          | 0.2348   |
| 1.8336        | 3.0   | 87   | 1.7319          | 0.1659   | 0.3182   | 0.2117      | 0.1611          | 0.3182          | 0.2133             | 0.2586       | 0.3182       | 0.3182          | 0.3182   |
| 1.452         | 4.0   | 116  | 1.5316          | 0.3336   | 0.4167   | 0.3752      | 0.3518          | 0.4167          | 0.3903             | 0.3682       | 0.4167       | 0.4167          | 0.4167   |
| 1.2545        | 5.0   | 145  | 1.4192          | 0.3999   | 0.4848   | 0.4447      | 0.4601          | 0.4848          | 0.5021             | 0.4318       | 0.4848       | 0.4848          | 0.4848   |
| 1.6479        | 6.0   | 174  | 1.3642          | 0.4649   | 0.5455   | 0.5265      | 0.5072          | 0.5455          | 0.5559             | 0.4750       | 0.5455       | 0.5455          | 0.5455   |
| 1.301         | 7.0   | 203  | 1.3015          | 0.4178   | 0.5303   | 0.4735      | 0.4090          | 0.5303          | 0.4503             | 0.4535       | 0.5303       | 0.5303          | 0.5303   |
| 0.9006        | 8.0   | 232  | 1.4861          | 0.4234   | 0.4924   | 0.4699      | 0.4586          | 0.4924          | 0.5286             | 0.4621       | 0.4924       | 0.4924          | 0.4924   |
| 0.4134        | 9.0   | 261  | 1.2101          | 0.4852   | 0.5833   | 0.5545      | 0.5427          | 0.5833          | 0.5894             | 0.5010       | 0.5833       | 0.5833          | 0.5833   |
| 0.9532        | 10.0  | 290  | 1.3783          | 0.4577   | 0.5682   | 0.5204      | 0.4557          | 0.5682          | 0.5160             | 0.4972       | 0.5682       | 0.5682          | 0.5682   |
| 0.4521        | 11.0  | 319  | 1.3602          | 0.5266   | 0.6136   | 0.5923      | 0.5296          | 0.6136          | 0.5907             | 0.5403       | 0.6136       | 0.6136          | 0.6136   |
| 0.633         | 12.0  | 348  | 1.4293          | 0.5032   | 0.5833   | 0.5727      | 0.4969          | 0.5833          | 0.5674             | 0.5140       | 0.5833       | 0.5833          | 0.5833   |
| 0.4268        | 13.0  | 377  | 1.4388          | 0.5031   | 0.5833   | 0.5676      | 0.5543          | 0.5833          | 0.6189             | 0.5124       | 0.5833       | 0.5833          | 0.5833   |
| 0.2857        | 14.0  | 406  | 1.6012          | 0.5071   | 0.5833   | 0.5676      | 0.5209          | 0.5833          | 0.5879             | 0.5211       | 0.5833       | 0.5833          | 0.5833   |
| 0.2606        | 15.0  | 435  | 1.5817          | 0.5579   | 0.6136   | 0.6109      | 0.5657          | 0.6136          | 0.6178             | 0.5590       | 0.6136       | 0.6136          | 0.6136   |
| 0.2028        | 16.0  | 464  | 1.8048          | 0.4526   | 0.5227   | 0.5112      | 0.4703          | 0.5227          | 0.5378             | 0.4668       | 0.5227       | 0.5227          | 0.5227   |
| 0.3251        | 17.0  | 493  | 1.6340          | 0.4942   | 0.5833   | 0.5625      | 0.5049          | 0.5833          | 0.5631             | 0.5031       | 0.5833       | 0.5833          | 0.5833   |
| 0.0369        | 18.0  | 522  | 1.5847          | 0.5860   | 0.6439   | 0.6349      | 0.6267          | 0.6439          | 0.6476             | 0.5824       | 0.6439       | 0.6439          | 0.6439   |
| 0.1133        | 19.0  | 551  | 1.5825          | 0.5457   | 0.6288   | 0.6157      | 0.5377          | 0.6288          | 0.6111             | 0.5615       | 0.6288       | 0.6288          | 0.6288   |
| 0.0457        | 20.0  | 580  | 1.7253          | 0.5258   | 0.6136   | 0.5938      | 0.5229          | 0.6136          | 0.5854             | 0.5391       | 0.6136       | 0.6136          | 0.6136   |
| 0.1109        | 21.0  | 609  | 1.7898          | 0.5708   | 0.6212   | 0.6154      | 0.6150          | 0.6212          | 0.6283             | 0.5613       | 0.6212       | 0.6212          | 0.6212   |
| 0.046         | 22.0  | 638  | 1.7368          | 0.5656   | 0.6136   | 0.6029      | 0.6021          | 0.6136          | 0.6103             | 0.5615       | 0.6136       | 0.6136          | 0.6136   |
| 0.0553        | 23.0  | 667  | 2.2478          | 0.4822   | 0.5682   | 0.5430      | 0.4851          | 0.5682          | 0.5380             | 0.4975       | 0.5682       | 0.5682          | 0.5682   |
| 0.0047        | 24.0  | 696  | 2.1705          | 0.5133   | 0.5909   | 0.5750      | 0.5158          | 0.5909          | 0.5716             | 0.5220       | 0.5909       | 0.5909          | 0.5909   |
| 0.0104        | 25.0  | 725  | 2.2669          | 0.4950   | 0.5833   | 0.5622      | 0.5035          | 0.5833          | 0.5609             | 0.5038       | 0.5833       | 0.5833          | 0.5833   |
| 0.0287        | 26.0  | 754  | 2.0390          | 0.5267   | 0.6061   | 0.5935      | 0.5265          | 0.6061          | 0.5898             | 0.5346       | 0.6061       | 0.6061          | 0.6061   |
| 0.0212        | 27.0  | 783  | 2.1345          | 0.5344   | 0.6136   | 0.6005      | 0.5308          | 0.6136          | 0.5946             | 0.5449       | 0.6136       | 0.6136          | 0.6136   |
| 0.0221        | 28.0  | 812  | 2.1555          | 0.5607   | 0.6136   | 0.6035      | 0.5953          | 0.6136          | 0.6107             | 0.5583       | 0.6136       | 0.6136          | 0.6136   |
| 0.001         | 29.0  | 841  | 2.1102          | 0.5833   | 0.6364   | 0.6289      | 0.6172          | 0.6364          | 0.6353             | 0.5789       | 0.6364       | 0.6364          | 0.6364   |
| 0.0045        | 30.0  | 870  | 2.0669          | 0.5862   | 0.6364   | 0.6290      | 0.6164          | 0.6364          | 0.6326             | 0.5831       | 0.6364       | 0.6364          | 0.6364   |
| 0.0021        | 31.0  | 899  | 2.1442          | 0.5833   | 0.6364   | 0.6282      | 0.6165          | 0.6364          | 0.6330             | 0.5789       | 0.6364       | 0.6364          | 0.6364   |
| 0.0014        | 32.0  | 928  | 2.1435          | 0.5616   | 0.6136   | 0.6049      | 0.5957          | 0.6136          | 0.6099             | 0.5569       | 0.6136       | 0.6136          | 0.6136   |
| 0.0016        | 33.0  | 957  | 2.1279          | 0.5621   | 0.6136   | 0.6047      | 0.5966          | 0.6136          | 0.6093             | 0.5569       | 0.6136       | 0.6136          | 0.6136   |
| 0.0008        | 34.0  | 986  | 2.1310          | 0.5691   | 0.6212   | 0.6127      | 0.6030          | 0.6212          | 0.6170             | 0.5641       | 0.6212       | 0.6212          | 0.6212   |
| 0.0006        | 35.0  | 1015 | 2.1338          | 0.5690   | 0.6212   | 0.6130      | 0.6026          | 0.6212          | 0.6176             | 0.5641       | 0.6212       | 0.6212          | 0.6212   |


### Framework versions

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

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