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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: square_run_32_batch
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_32_batch
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: 1.6241
- F1 Macro: 0.5019
- F1 Micro: 0.5758
- F1 Weighted: 0.5679
- Precision Macro: 0.5021
- Precision Micro: 0.5758
- Precision Weighted: 0.5657
- Recall Macro: 0.5073
- Recall Micro: 0.5758
- Recall Weighted: 0.5758
- Accuracy: 0.5758
## 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: 32
- eval_batch_size: 32
- 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: 30
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9373 | 1.0 | 15 | 1.8818 | 0.0464 | 0.1894 | 0.0615 | 0.0277 | 0.1894 | 0.0367 | 0.1429 | 0.1894 | 0.1894 | 0.1894 |
| 1.869 | 2.0 | 30 | 1.8642 | 0.1100 | 0.2652 | 0.1418 | 0.075 | 0.2652 | 0.0968 | 0.2063 | 0.2652 | 0.2652 | 0.2652 |
| 1.9218 | 3.0 | 45 | 1.8754 | 0.1163 | 0.2576 | 0.1460 | 0.1316 | 0.2576 | 0.1566 | 0.1905 | 0.2576 | 0.2576 | 0.2576 |
| 1.6733 | 4.0 | 60 | 1.6881 | 0.2445 | 0.3864 | 0.3053 | 0.2427 | 0.3864 | 0.2917 | 0.2992 | 0.3864 | 0.3864 | 0.3864 |
| 1.54 | 5.0 | 75 | 1.5528 | 0.3252 | 0.4242 | 0.3856 | 0.3429 | 0.4242 | 0.4101 | 0.3570 | 0.4242 | 0.4242 | 0.4242 |
| 1.4418 | 6.0 | 90 | 1.5737 | 0.2858 | 0.3864 | 0.3213 | 0.2846 | 0.3864 | 0.3243 | 0.3398 | 0.3864 | 0.3864 | 0.3864 |
| 0.8592 | 7.0 | 105 | 1.5408 | 0.3444 | 0.4394 | 0.3965 | 0.3208 | 0.4394 | 0.3674 | 0.3791 | 0.4394 | 0.4394 | 0.4394 |
| 1.1427 | 8.0 | 120 | 1.2804 | 0.4638 | 0.5606 | 0.5317 | 0.4698 | 0.5606 | 0.5280 | 0.4831 | 0.5606 | 0.5606 | 0.5606 |
| 0.7849 | 9.0 | 135 | 1.2880 | 0.4649 | 0.5530 | 0.5291 | 0.4804 | 0.5530 | 0.5401 | 0.4823 | 0.5530 | 0.5530 | 0.5530 |
| 0.6846 | 10.0 | 150 | 1.3130 | 0.4298 | 0.5152 | 0.4811 | 0.4404 | 0.5152 | 0.5005 | 0.4671 | 0.5152 | 0.5152 | 0.5152 |
| 0.4006 | 11.0 | 165 | 1.2958 | 0.4931 | 0.5833 | 0.5598 | 0.4983 | 0.5833 | 0.5756 | 0.5229 | 0.5833 | 0.5833 | 0.5833 |
| 0.4329 | 12.0 | 180 | 1.2990 | 0.5062 | 0.5530 | 0.5562 | 0.5315 | 0.5530 | 0.5874 | 0.5133 | 0.5530 | 0.5530 | 0.5530 |
| 0.482 | 13.0 | 195 | 1.3831 | 0.4842 | 0.5152 | 0.5233 | 0.5517 | 0.5152 | 0.5803 | 0.4839 | 0.5152 | 0.5152 | 0.5152 |
| 0.6409 | 14.0 | 210 | 1.4066 | 0.5081 | 0.5985 | 0.5765 | 0.5194 | 0.5985 | 0.5820 | 0.5232 | 0.5985 | 0.5985 | 0.5985 |
| 0.3206 | 15.0 | 225 | 1.3690 | 0.5155 | 0.5606 | 0.5520 | 0.6158 | 0.5606 | 0.5890 | 0.5170 | 0.5606 | 0.5606 | 0.5606 |
| 0.1773 | 16.0 | 240 | 1.2568 | 0.5920 | 0.6515 | 0.6408 | 0.6894 | 0.6515 | 0.6623 | 0.5843 | 0.6515 | 0.6515 | 0.6515 |
| 0.3259 | 17.0 | 255 | 1.3406 | 0.5467 | 0.6061 | 0.5961 | 0.5615 | 0.6061 | 0.6033 | 0.5467 | 0.6061 | 0.6061 | 0.6061 |
| 0.1123 | 18.0 | 270 | 1.3767 | 0.5868 | 0.6364 | 0.6306 | 0.6258 | 0.6364 | 0.6413 | 0.5785 | 0.6364 | 0.6364 | 0.6364 |
| 0.1129 | 19.0 | 285 | 1.4680 | 0.5879 | 0.6439 | 0.6306 | 0.6809 | 0.6439 | 0.6933 | 0.5806 | 0.6439 | 0.6439 | 0.6439 |
| 0.0651 | 20.0 | 300 | 1.4981 | 0.6655 | 0.6894 | 0.6876 | 0.7115 | 0.6894 | 0.7224 | 0.6511 | 0.6894 | 0.6894 | 0.6894 |
| 0.0685 | 21.0 | 315 | 1.4621 | 0.6091 | 0.6515 | 0.6494 | 0.6303 | 0.6515 | 0.6641 | 0.6040 | 0.6515 | 0.6515 | 0.6515 |
| 0.1469 | 22.0 | 330 | 1.5347 | 0.5330 | 0.6212 | 0.6040 | 0.5477 | 0.6212 | 0.6149 | 0.5440 | 0.6212 | 0.6212 | 0.6212 |
| 0.0289 | 23.0 | 345 | 1.5417 | 0.5466 | 0.6288 | 0.6180 | 0.5409 | 0.6288 | 0.6108 | 0.5549 | 0.6288 | 0.6288 | 0.6288 |
| 0.01 | 24.0 | 360 | 1.5670 | 0.5475 | 0.6364 | 0.6187 | 0.5435 | 0.6364 | 0.6104 | 0.5594 | 0.6364 | 0.6364 | 0.6364 |
| 0.035 | 25.0 | 375 | 1.6037 | 0.5529 | 0.6364 | 0.6209 | 0.5470 | 0.6364 | 0.6156 | 0.5679 | 0.6364 | 0.6364 | 0.6364 |
| 0.0109 | 26.0 | 390 | 1.6752 | 0.5897 | 0.6212 | 0.6203 | 0.6145 | 0.6212 | 0.6527 | 0.6000 | 0.6212 | 0.6212 | 0.6212 |
| 0.038 | 27.0 | 405 | 1.6724 | 0.5344 | 0.6136 | 0.6008 | 0.5332 | 0.6136 | 0.6005 | 0.5468 | 0.6136 | 0.6136 | 0.6136 |
| 0.0116 | 28.0 | 420 | 1.6252 | 0.5384 | 0.6212 | 0.6090 | 0.5337 | 0.6212 | 0.6033 | 0.5491 | 0.6212 | 0.6212 | 0.6212 |
| 0.006 | 29.0 | 435 | 1.5980 | 0.5572 | 0.6364 | 0.6294 | 0.5529 | 0.6364 | 0.6246 | 0.5634 | 0.6364 | 0.6364 | 0.6364 |
| 0.0046 | 30.0 | 450 | 1.5939 | 0.5605 | 0.6439 | 0.6342 | 0.5546 | 0.6439 | 0.6269 | 0.5687 | 0.6439 | 0.6439 | 0.6439 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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