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---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: squarerun_large_model
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. -->
# 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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