
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# square_run_min_loss

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: 1.5286
- F1 Macro: 0.4619
- F1 Micro: 0.5455
- F1 Weighted: 0.5156
- Precision Macro: 0.4696
- Precision Micro: 0.5455
- Precision Weighted: 0.5176
- Recall Macro: 0.4841
- 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: 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.934         | 1.0   | 58   | 1.8780          | 0.0664   | 0.2045   | 0.0901      | 0.1708          | 0.2045          | 0.2415             | 0.1534       | 0.2045       | 0.2045          | 0.2045   |
| 1.8145        | 2.0   | 116  | 1.8828          | 0.0691   | 0.1742   | 0.0755      | 0.0608          | 0.1742          | 0.0658             | 0.1575       | 0.1742       | 0.1742          | 0.1742   |
| 1.8527        | 3.0   | 174  | 1.7131          | 0.2503   | 0.3788   | 0.3053      | 0.2573          | 0.3788          | 0.3062             | 0.3094       | 0.3788       | 0.3788          | 0.3788   |
| 1.6734        | 4.0   | 232  | 1.7940          | 0.1621   | 0.2803   | 0.2087      | 0.2145          | 0.2803          | 0.2624             | 0.2076       | 0.2803       | 0.2803          | 0.2803   |
| 1.6408        | 5.0   | 290  | 1.6808          | 0.1570   | 0.3333   | 0.1965      | 0.1432          | 0.3333          | 0.1858             | 0.2702       | 0.3333       | 0.3333          | 0.3333   |
| 1.5696        | 6.0   | 348  | 1.5061          | 0.3172   | 0.4470   | 0.3802      | 0.3895          | 0.4470          | 0.4186             | 0.3618       | 0.4470       | 0.4470          | 0.4470   |
| 1.4543        | 7.0   | 406  | 1.3674          | 0.4113   | 0.5152   | 0.4708      | 0.4077          | 0.5152          | 0.4630             | 0.4479       | 0.5152       | 0.5152          | 0.5152   |
| 1.2349        | 8.0   | 464  | 1.3137          | 0.4024   | 0.5      | 0.4550      | 0.4050          | 0.5             | 0.4606             | 0.4479       | 0.5          | 0.5             | 0.5      |
| 1.2544        | 9.0   | 522  | 1.3322          | 0.4209   | 0.5076   | 0.4748      | 0.4224          | 0.5076          | 0.4737             | 0.4480       | 0.5076       | 0.5076          | 0.5076   |
| 1.206         | 10.0  | 580  | 1.3818          | 0.3555   | 0.4621   | 0.4009      | 0.3931          | 0.4621          | 0.4372             | 0.4129       | 0.4621       | 0.4621          | 0.4621   |
| 1.0416        | 11.0  | 638  | 1.3142          | 0.4610   | 0.5606   | 0.5249      | 0.5218          | 0.5606          | 0.5872             | 0.4951       | 0.5606       | 0.5606          | 0.5606   |
| 1.1494        | 12.0  | 696  | 1.3793          | 0.4106   | 0.4773   | 0.4652      | 0.4619          | 0.4773          | 0.5256             | 0.4227       | 0.4773       | 0.4773          | 0.4773   |
| 0.7366        | 13.0  | 754  | 1.1936          | 0.5656   | 0.6515   | 0.6383      | 0.5708          | 0.6515          | 0.6446             | 0.5790       | 0.6515       | 0.6515          | 0.6515   |
| 1.3729        | 14.0  | 812  | 1.2285          | 0.5151   | 0.6061   | 0.5861      | 0.5714          | 0.6061          | 0.6314             | 0.5225       | 0.6061       | 0.6061          | 0.6061   |
| 1.3638        | 15.0  | 870  | 1.1742          | 0.5389   | 0.6212   | 0.6055      | 0.5617          | 0.6212          | 0.6334             | 0.5513       | 0.6212       | 0.6212          | 0.6212   |
| 0.9063        | 16.0  | 928  | 1.2325          | 0.5079   | 0.5985   | 0.5770      | 0.5077          | 0.5985          | 0.5715             | 0.5215       | 0.5985       | 0.5985          | 0.5985   |
| 0.4584        | 17.0  | 986  | 1.1497          | 0.5515   | 0.6364   | 0.6210      | 0.5676          | 0.6364          | 0.6286             | 0.5575       | 0.6364       | 0.6364          | 0.6364   |
| 0.86          | 18.0  | 1044 | 1.2673          | 0.4925   | 0.5909   | 0.5719      | 0.4968          | 0.5909          | 0.5681             | 0.5031       | 0.5909       | 0.5909          | 0.5909   |
| 0.2113        | 19.0  | 1102 | 1.2132          | 0.5180   | 0.6212   | 0.5986      | 0.5386          | 0.6212          | 0.6049             | 0.5257       | 0.6212       | 0.6212          | 0.6212   |
| 0.1168        | 20.0  | 1160 | 1.2442          | 0.5543   | 0.6136   | 0.6070      | 0.5742          | 0.6136          | 0.6164             | 0.5517       | 0.6136       | 0.6136          | 0.6136   |
| 0.3149        | 21.0  | 1218 | 1.2900          | 0.5446   | 0.6288   | 0.6146      | 0.5463          | 0.6288          | 0.6120             | 0.5534       | 0.6288       | 0.6288          | 0.6288   |
| 0.0793        | 22.0  | 1276 | 1.3290          | 0.5692   | 0.6288   | 0.6210      | 0.5960          | 0.6288          | 0.6359             | 0.5651       | 0.6288       | 0.6288          | 0.6288   |
| 0.1761        | 23.0  | 1334 | 1.4284          | 0.5572   | 0.6212   | 0.6032      | 0.6454          | 0.6212          | 0.6563             | 0.5516       | 0.6212       | 0.6212          | 0.6212   |
| 0.1714        | 24.0  | 1392 | 1.2994          | 0.5782   | 0.6288   | 0.6344      | 0.5899          | 0.6288          | 0.6461             | 0.5728       | 0.6288       | 0.6288          | 0.6288   |
| 0.465         | 25.0  | 1450 | 1.4011          | 0.5581   | 0.6136   | 0.6134      | 0.5662          | 0.6136          | 0.6188             | 0.5556       | 0.6136       | 0.6136          | 0.6136   |
| 0.2203        | 26.0  | 1508 | 1.4701          | 0.5741   | 0.6288   | 0.6266      | 0.6167          | 0.6288          | 0.6553             | 0.5676       | 0.6288       | 0.6288          | 0.6288   |
| 0.0574        | 27.0  | 1566 | 1.4511          | 0.5800   | 0.6364   | 0.6352      | 0.6073          | 0.6364          | 0.6546             | 0.5738       | 0.6364       | 0.6364          | 0.6364   |
| 0.0399        | 28.0  | 1624 | 1.4921          | 0.5674   | 0.6061   | 0.6133      | 0.5933          | 0.6061          | 0.6390             | 0.5645       | 0.6061       | 0.6061          | 0.6061   |
| 0.0269        | 29.0  | 1682 | 1.4752          | 0.5563   | 0.6288   | 0.6283      | 0.5686          | 0.6288          | 0.6350             | 0.5515       | 0.6288       | 0.6288          | 0.6288   |
| 0.0267        | 30.0  | 1740 | 1.5353          | 0.5621   | 0.6136   | 0.6142      | 0.5859          | 0.6136          | 0.6324             | 0.5565       | 0.6136       | 0.6136          | 0.6136   |
| 0.1094        | 31.0  | 1798 | 1.5126          | 0.5912   | 0.6515   | 0.6529      | 0.6028          | 0.6515          | 0.6604             | 0.5867       | 0.6515       | 0.6515          | 0.6515   |
| 0.0243        | 32.0  | 1856 | 1.4900          | 0.5985   | 0.6591   | 0.6563      | 0.6103          | 0.6591          | 0.6604             | 0.5935       | 0.6591       | 0.6591          | 0.6591   |
| 0.0366        | 33.0  | 1914 | 1.4680          | 0.6275   | 0.6894   | 0.6851      | 0.6369          | 0.6894          | 0.6855             | 0.6241       | 0.6894       | 0.6894          | 0.6894   |
| 0.0235        | 34.0  | 1972 | 1.4772          | 0.6216   | 0.6818   | 0.6795      | 0.6324          | 0.6818          | 0.6836             | 0.6173       | 0.6818       | 0.6818          | 0.6818   |
| 0.0345        | 35.0  | 2030 | 1.4754          | 0.6556   | 0.6970   | 0.6961      | 0.6722          | 0.6970          | 0.7038             | 0.6479       | 0.6970       | 0.6970          | 0.6970   |


### Framework versions

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

- 0 1 2 3 4 5