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---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-tiny-patch16-224
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. -->
# vit-tiny-patch16-224
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6141
- F1 Macro: 0.4385
- F1 Micro: 0.5303
- F1 Weighted: 0.4856
- Precision Macro: 0.5225
- Precision Micro: 0.5303
- Precision Weighted: 0.5788
- Recall Macro: 0.4858
- Recall Micro: 0.5303
- Recall Weighted: 0.5303
- Accuracy: 0.5303
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9719 | 1.0 | 29 | 1.9209 | 0.0949 | 0.2197 | 0.1155 | 0.0891 | 0.2197 | 0.1051 | 0.1722 | 0.2197 | 0.2197 | 0.2197 |
| 1.8717 | 2.0 | 58 | 2.0378 | 0.0953 | 0.1970 | 0.1069 | 0.1996 | 0.1970 | 0.2660 | 0.1794 | 0.1970 | 0.1970 | 0.1970 |
| 1.9326 | 3.0 | 87 | 1.7680 | 0.2290 | 0.3939 | 0.2939 | 0.2151 | 0.3939 | 0.2682 | 0.3004 | 0.3939 | 0.3939 | 0.3939 |
| 1.2873 | 4.0 | 116 | 1.5892 | 0.3502 | 0.4470 | 0.4082 | 0.4831 | 0.4470 | 0.5140 | 0.3646 | 0.4470 | 0.4470 | 0.4470 |
| 1.3997 | 5.0 | 145 | 1.4773 | 0.3481 | 0.5 | 0.4245 | 0.3463 | 0.5 | 0.4119 | 0.4052 | 0.5 | 0.5 | 0.5 |
| 1.7041 | 6.0 | 174 | 1.4406 | 0.4266 | 0.5379 | 0.5005 | 0.5011 | 0.5379 | 0.5628 | 0.4529 | 0.5379 | 0.5379 | 0.5379 |
| 1.1863 | 7.0 | 203 | 1.3680 | 0.4759 | 0.5682 | 0.5400 | 0.5559 | 0.5682 | 0.6032 | 0.4831 | 0.5682 | 0.5682 | 0.5682 |
| 0.9817 | 8.0 | 232 | 1.3515 | 0.4399 | 0.5227 | 0.4969 | 0.4445 | 0.5227 | 0.5088 | 0.4722 | 0.5227 | 0.5227 | 0.5227 |
| 0.617 | 9.0 | 261 | 1.3867 | 0.4895 | 0.5909 | 0.5555 | 0.5136 | 0.5909 | 0.5776 | 0.5183 | 0.5909 | 0.5909 | 0.5909 |
| 1.0365 | 10.0 | 290 | 1.4607 | 0.4313 | 0.5379 | 0.4961 | 0.4371 | 0.5379 | 0.4997 | 0.4674 | 0.5379 | 0.5379 | 0.5379 |
| 0.6815 | 11.0 | 319 | 1.3133 | 0.4962 | 0.5909 | 0.5664 | 0.5087 | 0.5909 | 0.5742 | 0.5133 | 0.5909 | 0.5909 | 0.5909 |
| 0.4153 | 12.0 | 348 | 1.3528 | 0.5082 | 0.5909 | 0.5735 | 0.5185 | 0.5909 | 0.5820 | 0.5202 | 0.5909 | 0.5909 | 0.5909 |
| 0.3396 | 13.0 | 377 | 1.3856 | 0.5372 | 0.5909 | 0.5830 | 0.5623 | 0.5909 | 0.6018 | 0.5387 | 0.5909 | 0.5909 | 0.5909 |
| 0.5415 | 14.0 | 406 | 1.4252 | 0.5132 | 0.5909 | 0.5795 | 0.5223 | 0.5909 | 0.5893 | 0.5255 | 0.5909 | 0.5909 | 0.5909 |
| 0.4421 | 15.0 | 435 | 1.4081 | 0.5574 | 0.6136 | 0.6086 | 0.5753 | 0.6136 | 0.6149 | 0.5532 | 0.6136 | 0.6136 | 0.6136 |
| 0.2893 | 16.0 | 464 | 1.5285 | 0.5127 | 0.5985 | 0.5833 | 0.5059 | 0.5985 | 0.5752 | 0.5253 | 0.5985 | 0.5985 | 0.5985 |
| 0.2403 | 17.0 | 493 | 1.4820 | 0.5395 | 0.6288 | 0.6065 | 0.5808 | 0.6288 | 0.6380 | 0.5460 | 0.6288 | 0.6288 | 0.6288 |
| 0.1087 | 18.0 | 522 | 1.3999 | 0.5320 | 0.6061 | 0.6009 | 0.5612 | 0.6061 | 0.6211 | 0.5261 | 0.6061 | 0.6061 | 0.6061 |
| 0.2619 | 19.0 | 551 | 1.4408 | 0.5618 | 0.6136 | 0.6037 | 0.6154 | 0.6136 | 0.6225 | 0.5501 | 0.6136 | 0.6136 | 0.6136 |
| 0.1154 | 20.0 | 580 | 1.4516 | 0.5402 | 0.6288 | 0.6090 | 0.5538 | 0.6288 | 0.6145 | 0.5492 | 0.6288 | 0.6288 | 0.6288 |
| 0.1367 | 21.0 | 609 | 1.5306 | 0.5254 | 0.6136 | 0.5942 | 0.5321 | 0.6136 | 0.5923 | 0.5340 | 0.6136 | 0.6136 | 0.6136 |
| 0.0839 | 22.0 | 638 | 1.6397 | 0.5154 | 0.5833 | 0.5756 | 0.5274 | 0.5833 | 0.5895 | 0.5252 | 0.5833 | 0.5833 | 0.5833 |
| 0.1818 | 23.0 | 667 | 1.6416 | 0.5656 | 0.6515 | 0.6359 | 0.5848 | 0.6515 | 0.6456 | 0.5696 | 0.6515 | 0.6515 | 0.6515 |
| 0.0781 | 24.0 | 696 | 1.6026 | 0.5393 | 0.6212 | 0.6079 | 0.5524 | 0.6212 | 0.6118 | 0.5412 | 0.6212 | 0.6212 | 0.6212 |
| 0.0792 | 25.0 | 725 | 1.5997 | 0.5494 | 0.6288 | 0.6180 | 0.5716 | 0.6288 | 0.6297 | 0.5480 | 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
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