
<!-- 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_with_actual_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: 1.2489
- F1 Macro: 0.5325
- F1 Micro: 0.6212
- F1 Weighted: 0.6021
- Precision Macro: 0.5262
- Precision Micro: 0.6212
- Precision Weighted: 0.5980
- Recall Macro: 0.5529
- Recall Micro: 0.6212
- Recall Weighted: 0.6212
- Accuracy: 0.6212

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9666        | 1.0     | 15   | 1.9122          | 0.0522   | 0.1818   | 0.0701      | 0.0544          | 0.1818          | 0.0752             | 0.1367       | 0.1818       | 0.1818          | 0.1818   |
| 1.3456        | 2.0     | 30   | 1.8989          | 0.1141   | 0.2045   | 0.1357      | 0.0978          | 0.2045          | 0.1212             | 0.1792       | 0.2045       | 0.2045          | 0.2045   |
| 1.8934        | 3.0     | 45   | 1.8013          | 0.1472   | 0.25     | 0.1719      | 0.2585          | 0.25            | 0.3007             | 0.1956       | 0.25         | 0.25            | 0.25     |
| 1.0877        | 4.0     | 60   | 1.5598          | 0.3297   | 0.3864   | 0.3581      | 0.3466          | 0.3864          | 0.3737             | 0.3502       | 0.3864       | 0.3864          | 0.3864   |
| 1.2233        | 5.0     | 75   | 1.4987          | 0.3442   | 0.4318   | 0.3877      | 0.4069          | 0.4318          | 0.4401             | 0.3723       | 0.4318       | 0.4318          | 0.4318   |
| 1.0714        | 6.0     | 90   | 1.4548          | 0.3909   | 0.4848   | 0.4447      | 0.4532          | 0.4848          | 0.5043             | 0.4156       | 0.4848       | 0.4848          | 0.4848   |
| 1.0008        | 7.0     | 105  | 1.3781          | 0.4699   | 0.5455   | 0.5277      | 0.4927          | 0.5455          | 0.5384             | 0.4728       | 0.5455       | 0.5455          | 0.5455   |
| 0.8822        | 8.0     | 120  | 1.4939          | 0.4440   | 0.5076   | 0.4959      | 0.4501          | 0.5076          | 0.5066             | 0.4594       | 0.5076       | 0.5076          | 0.5076   |
| 0.6024        | 9.0     | 135  | 1.2516          | 0.4922   | 0.5682   | 0.5559      | 0.4841          | 0.5682          | 0.5477             | 0.5037       | 0.5682       | 0.5682          | 0.5682   |
| 0.2719        | 10.0    | 150  | 1.4020          | 0.4582   | 0.5455   | 0.5177      | 0.4610          | 0.5455          | 0.5195             | 0.4821       | 0.5455       | 0.5455          | 0.5455   |
| 0.2165        | 11.0    | 165  | 1.4121          | 0.4974   | 0.5758   | 0.5601      | 0.4906          | 0.5758          | 0.5530             | 0.5121       | 0.5758       | 0.5758          | 0.5758   |
| 0.2752        | 12.0    | 180  | 1.4315          | 0.5750   | 0.5909   | 0.5923      | 0.6618          | 0.5909          | 0.6319             | 0.5545       | 0.5909       | 0.5909          | 0.5909   |
| 0.4386        | 13.0    | 195  | 1.4823          | 0.5170   | 0.5606   | 0.5607      | 0.5515          | 0.5606          | 0.5955             | 0.5150       | 0.5606       | 0.5606          | 0.5606   |
| 0.2152        | 14.0    | 210  | 1.4962          | 0.5371   | 0.5833   | 0.5795      | 0.5796          | 0.5833          | 0.6003             | 0.5334       | 0.5833       | 0.5833          | 0.5833   |
| 0.2021        | 15.0    | 225  | 1.5027          | 0.4698   | 0.5606   | 0.5383      | 0.4781          | 0.5606          | 0.5353             | 0.4829       | 0.5606       | 0.5606          | 0.5606   |
| 0.1147        | 16.0    | 240  | 1.5977          | 0.4771   | 0.5606   | 0.5447      | 0.4901          | 0.5606          | 0.5638             | 0.4976       | 0.5606       | 0.5606          | 0.5606   |
| 0.0648        | 17.0    | 255  | 1.6214          | 0.4894   | 0.5682   | 0.5551      | 0.4829          | 0.5682          | 0.5474             | 0.5009       | 0.5682       | 0.5682          | 0.5682   |
| 0.0958        | 18.0    | 270  | 1.6267          | 0.4911   | 0.5379   | 0.5332      | 0.5140          | 0.5379          | 0.5539             | 0.4899       | 0.5379       | 0.5379          | 0.5379   |
| 0.0468        | 19.0    | 285  | 1.6385          | 0.5462   | 0.6061   | 0.5975      | 0.5685          | 0.6061          | 0.6104             | 0.5458       | 0.6061       | 0.6061          | 0.6061   |
| 0.0344        | 20.0    | 300  | 1.7048          | 0.5815   | 0.6136   | 0.6076      | 0.6000          | 0.6136          | 0.6225             | 0.5844       | 0.6136       | 0.6136          | 0.6136   |
| 0.1686        | 21.0    | 315  | 1.8050          | 0.5545   | 0.6061   | 0.5975      | 0.5939          | 0.6061          | 0.6116             | 0.5538       | 0.6061       | 0.6061          | 0.6061   |
| 0.044         | 22.0    | 330  | 1.7228          | 0.5362   | 0.5909   | 0.5779      | 0.5746          | 0.5909          | 0.5894             | 0.5380       | 0.5909       | 0.5909          | 0.5909   |
| 0.0185        | 23.0    | 345  | 1.9158          | 0.5024   | 0.6061   | 0.5756      | 0.5249          | 0.6061          | 0.6034             | 0.5363       | 0.6061       | 0.6061          | 0.6061   |
| 0.0065        | 24.0    | 360  | 1.7524          | 0.5881   | 0.6212   | 0.6111      | 0.6132          | 0.6212          | 0.6234             | 0.5850       | 0.6212       | 0.6212          | 0.6212   |
| 0.1169        | 25.0    | 375  | 1.7258          | 0.5801   | 0.6364   | 0.6246      | 0.6131          | 0.6364          | 0.6282             | 0.5780       | 0.6364       | 0.6364          | 0.6364   |
| 0.0092        | 26.0    | 390  | 1.8467          | 0.5427   | 0.5985   | 0.5849      | 0.5862          | 0.5985          | 0.5954             | 0.5379       | 0.5985       | 0.5985          | 0.5985   |
| 0.0286        | 27.0    | 405  | 1.8018          | 0.5670   | 0.6212   | 0.6108      | 0.6027          | 0.6212          | 0.6171             | 0.5636       | 0.6212       | 0.6212          | 0.6212   |
| 0.0263        | 28.0    | 420  | 1.8319          | 0.5621   | 0.6212   | 0.6079      | 0.5969          | 0.6212          | 0.6121             | 0.5598       | 0.6212       | 0.6212          | 0.6212   |
| 0.0049        | 29.0    | 435  | 1.8276          | 0.5637   | 0.6136   | 0.6066      | 0.5962          | 0.6136          | 0.6099             | 0.5574       | 0.6136       | 0.6136          | 0.6136   |
| 0.0039        | 30.0    | 450  | 1.8631          | 0.5480   | 0.5985   | 0.5900      | 0.5879          | 0.5985          | 0.5988             | 0.5405       | 0.5985       | 0.5985          | 0.5985   |
| 0.0636        | 31.0    | 465  | 1.8579          | 0.5697   | 0.6212   | 0.6118      | 0.6075          | 0.6212          | 0.6181             | 0.5629       | 0.6212       | 0.6212          | 0.6212   |
| 0.0023        | 32.0    | 480  | 1.8398          | 0.5773   | 0.6288   | 0.6204      | 0.6144          | 0.6288          | 0.6265             | 0.57         | 0.6288       | 0.6288          | 0.6288   |
| 0.0028        | 32.6897 | 490  | 1.8415          | 0.5773   | 0.6288   | 0.6204      | 0.6144          | 0.6288          | 0.6265             | 0.57         | 0.6288       | 0.6288          | 0.6288   |


### 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