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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: squarerun
results: []
datasets:
- corranm/first_vote_100_full_pic_without_vote_highlight_square
---
<!-- 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
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: 1.3394
- F1 Macro: 0.4627
- F1 Micro: 0.5606
- F1 Weighted: 0.5294
- Precision Macro: 0.4704
- Precision Micro: 0.5606
- Precision Weighted: 0.5310
- Recall Macro: 0.4855
- Recall Micro: 0.5606
- Recall Weighted: 0.5606
- Accuracy: 0.5606
## 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: 45
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.903 | 1.0 | 29 | 1.8868 | 0.0658 | 0.1742 | 0.0900 | 0.0502 | 0.1742 | 0.0693 | 0.1293 | 0.1742 | 0.1742 | 0.1742 |
| 1.8662 | 2.0 | 58 | 1.8740 | 0.0754 | 0.2197 | 0.1004 | 0.0603 | 0.2197 | 0.0773 | 0.1580 | 0.2197 | 0.2197 | 0.2197 |
| 1.9291 | 3.0 | 87 | 1.8862 | 0.0485 | 0.2045 | 0.0695 | 0.0292 | 0.2045 | 0.0418 | 0.1429 | 0.2045 | 0.2045 | 0.2045 |
| 1.7838 | 4.0 | 116 | 1.8127 | 0.1171 | 0.2652 | 0.1474 | 0.1092 | 0.2652 | 0.1321 | 0.1973 | 0.2652 | 0.2652 | 0.2652 |
| 1.7113 | 5.0 | 145 | 1.6979 | 0.2133 | 0.3485 | 0.2592 | 0.3189 | 0.3485 | 0.3631 | 0.2822 | 0.3485 | 0.3485 | 0.3485 |
| 1.6459 | 6.0 | 174 | 1.5577 | 0.2714 | 0.3939 | 0.3225 | 0.4296 | 0.3939 | 0.4531 | 0.3198 | 0.3939 | 0.3939 | 0.3939 |
| 1.4829 | 7.0 | 203 | 1.3814 | 0.4069 | 0.5227 | 0.4611 | 0.3786 | 0.5227 | 0.4216 | 0.4511 | 0.5227 | 0.5227 | 0.5227 |
| 1.2847 | 8.0 | 232 | 1.3783 | 0.3675 | 0.4545 | 0.4176 | 0.4992 | 0.4545 | 0.5702 | 0.4080 | 0.4545 | 0.4545 | 0.4545 |
| 0.7746 | 9.0 | 261 | 1.1536 | 0.4579 | 0.5758 | 0.5298 | 0.5301 | 0.5758 | 0.5896 | 0.4853 | 0.5758 | 0.5758 | 0.5758 |
| 1.0172 | 10.0 | 290 | 1.2211 | 0.4700 | 0.5909 | 0.5365 | 0.5722 | 0.5909 | 0.6399 | 0.5182 | 0.5909 | 0.5909 | 0.5909 |
| 0.7865 | 11.0 | 319 | 1.1357 | 0.5282 | 0.6136 | 0.5961 | 0.5342 | 0.6136 | 0.6009 | 0.5432 | 0.6136 | 0.6136 | 0.6136 |
| 0.8335 | 12.0 | 348 | 1.1530 | 0.5315 | 0.6061 | 0.6017 | 0.5365 | 0.6061 | 0.6209 | 0.5489 | 0.6061 | 0.6061 | 0.6061 |
| 0.6959 | 13.0 | 377 | 1.1307 | 0.5638 | 0.6667 | 0.6451 | 0.5912 | 0.6667 | 0.6615 | 0.5773 | 0.6667 | 0.6667 | 0.6667 |
| 0.5864 | 14.0 | 406 | 1.1957 | 0.5211 | 0.5985 | 0.5894 | 0.5537 | 0.5985 | 0.6275 | 0.5389 | 0.5985 | 0.5985 | 0.5985 |
| 0.6145 | 15.0 | 435 | 0.9957 | 0.6086 | 0.7045 | 0.6833 | 0.6164 | 0.7045 | 0.6791 | 0.6160 | 0.7045 | 0.7045 | 0.7045 |
| 0.5632 | 16.0 | 464 | 1.2302 | 0.5112 | 0.5985 | 0.5781 | 0.5219 | 0.5985 | 0.5853 | 0.5236 | 0.5985 | 0.5985 | 0.5985 |
| 0.3392 | 17.0 | 493 | 1.1925 | 0.5335 | 0.6288 | 0.6043 | 0.5903 | 0.6288 | 0.6435 | 0.5355 | 0.6288 | 0.6288 | 0.6288 |
| 0.2998 | 18.0 | 522 | 1.1444 | 0.5544 | 0.6364 | 0.6251 | 0.5520 | 0.6364 | 0.6248 | 0.5670 | 0.6364 | 0.6364 | 0.6364 |
| 0.2706 | 19.0 | 551 | 1.1072 | 0.5579 | 0.6439 | 0.6308 | 0.5790 | 0.6439 | 0.6404 | 0.5571 | 0.6439 | 0.6439 | 0.6439 |
| 0.2012 | 20.0 | 580 | 1.1353 | 0.5278 | 0.6212 | 0.6012 | 0.5433 | 0.6212 | 0.6063 | 0.5346 | 0.6212 | 0.6212 | 0.6212 |
| 0.532 | 21.0 | 609 | 1.2503 | 0.5421 | 0.6212 | 0.6079 | 0.5651 | 0.6212 | 0.6253 | 0.5488 | 0.6212 | 0.6212 | 0.6212 |
| 0.0963 | 22.0 | 638 | 1.2203 | 0.5702 | 0.6288 | 0.6227 | 0.5807 | 0.6288 | 0.6327 | 0.5745 | 0.6288 | 0.6288 | 0.6288 |
| 0.1076 | 23.0 | 667 | 1.3798 | 0.5216 | 0.6136 | 0.5894 | 0.5339 | 0.6136 | 0.5971 | 0.5370 | 0.6136 | 0.6136 | 0.6136 |
| 0.1773 | 24.0 | 696 | 1.3129 | 0.5422 | 0.6288 | 0.6169 | 0.5581 | 0.6288 | 0.6253 | 0.5453 | 0.6288 | 0.6288 | 0.6288 |
| 0.0598 | 25.0 | 725 | 1.2855 | 0.5633 | 0.6515 | 0.6381 | 0.5846 | 0.6515 | 0.6562 | 0.5713 | 0.6515 | 0.6515 | 0.6515 |
| 0.0632 | 26.0 | 754 | 1.3155 | 0.6414 | 0.6591 | 0.6643 | 0.6525 | 0.6591 | 0.6925 | 0.6585 | 0.6591 | 0.6591 | 0.6591 |
| 0.0644 | 27.0 | 783 | 1.3211 | 0.5588 | 0.6439 | 0.6315 | 0.5745 | 0.6439 | 0.6357 | 0.5595 | 0.6439 | 0.6439 | 0.6439 |
| 0.1495 | 28.0 | 812 | 1.4196 | 0.5539 | 0.6364 | 0.6245 | 0.5650 | 0.6364 | 0.6270 | 0.5556 | 0.6364 | 0.6364 | 0.6364 |
| 0.0413 | 29.0 | 841 | 1.4027 | 0.5378 | 0.6136 | 0.6102 | 0.5405 | 0.6136 | 0.6100 | 0.5380 | 0.6136 | 0.6136 | 0.6136 |
| 0.0323 | 30.0 | 870 | 1.4302 | 0.5641 | 0.6364 | 0.6329 | 0.5689 | 0.6364 | 0.6430 | 0.5712 | 0.6364 | 0.6364 | 0.6364 |
| 0.0452 | 31.0 | 899 | 1.4577 | 0.5706 | 0.6515 | 0.6412 | 0.5835 | 0.6515 | 0.6478 | 0.5738 | 0.6515 | 0.6515 | 0.6515 |
| 0.0285 | 32.0 | 928 | 1.4224 | 0.5597 | 0.6439 | 0.6300 | 0.5618 | 0.6439 | 0.6250 | 0.5657 | 0.6439 | 0.6439 | 0.6439 |
| 0.0241 | 33.0 | 957 | 1.4513 | 0.5542 | 0.6364 | 0.6252 | 0.5700 | 0.6364 | 0.6309 | 0.5533 | 0.6364 | 0.6364 | 0.6364 |
| 0.0224 | 34.0 | 986 | 1.4701 | 0.5795 | 0.6742 | 0.6545 | 0.5856 | 0.6742 | 0.6523 | 0.5902 | 0.6742 | 0.6742 | 0.6742 |
| 0.0228 | 35.0 | 1015 | 1.4697 | 0.5772 | 0.6591 | 0.6489 | 0.5870 | 0.6591 | 0.6497 | 0.5774 | 0.6591 | 0.6591 | 0.6591 |
| 0.0231 | 36.0 | 1044 | 1.5315 | 0.5745 | 0.6591 | 0.6491 | 0.5783 | 0.6591 | 0.6483 | 0.5788 | 0.6591 | 0.6591 | 0.6591 |
| 0.0457 | 37.0 | 1073 | 1.5210 | 0.5532 | 0.6439 | 0.6277 | 0.5641 | 0.6439 | 0.6317 | 0.5606 | 0.6439 | 0.6439 | 0.6439 |
| 0.0197 | 38.0 | 1102 | 1.4956 | 0.5636 | 0.6515 | 0.6386 | 0.5590 | 0.6515 | 0.6296 | 0.5714 | 0.6515 | 0.6515 | 0.6515 |
| 0.0219 | 39.0 | 1131 | 1.4910 | 0.5981 | 0.6591 | 0.6540 | 0.6063 | 0.6591 | 0.6554 | 0.5970 | 0.6591 | 0.6591 | 0.6591 |
| 0.0212 | 40.0 | 1160 | 1.5050 | 0.5912 | 0.6515 | 0.6462 | 0.5997 | 0.6515 | 0.6472 | 0.5898 | 0.6515 | 0.6515 | 0.6515 |
| 0.0212 | 41.0 | 1189 | 1.5091 | 0.5977 | 0.6591 | 0.6537 | 0.6080 | 0.6591 | 0.6558 | 0.5955 | 0.6591 | 0.6591 | 0.6591 |
| 0.0202 | 42.0 | 1218 | 1.4961 | 0.5655 | 0.6515 | 0.6411 | 0.5708 | 0.6515 | 0.6411 | 0.5695 | 0.6515 | 0.6515 | 0.6515 |
| 0.0216 | 43.0 | 1247 | 1.4917 | 0.5655 | 0.6515 | 0.6411 | 0.5708 | 0.6515 | 0.6411 | 0.5695 | 0.6515 | 0.6515 | 0.6515 |
| 0.0199 | 44.0 | 1276 | 1.4855 | 0.5674 | 0.6515 | 0.6423 | 0.5694 | 0.6515 | 0.6401 | 0.5717 | 0.6515 | 0.6515 | 0.6515 |
| 0.027 | 45.0 | 1305 | 1.4832 | 0.5674 | 0.6515 | 0.6423 | 0.5694 | 0.6515 | 0.6401 | 0.5717 | 0.6515 | 0.6515 | 0.6515 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |