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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: square_run_min_loss
results: []
---
<!-- 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
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