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# vit-base-patch16-224-in21k_16batch

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: 1.2813
- F1 Macro: 0.4280
- F1 Micro: 0.5455
- F1 Weighted: 0.4882
- Precision Macro: 0.4004
- Precision Micro: 0.5455
- Precision Weighted: 0.4529
- Recall Macro: 0.4762
- Recall Micro: 0.5455
- Recall Weighted: 0.5455
- Accuracy: 0.5455

## Model description

Using a batch size of 16

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- 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: 40

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9371        | 1.0   | 29   | 1.9372          | 0.0504   | 0.1212   | 0.0604      | 0.0334          | 0.1212          | 0.0403             | 0.1029       | 0.1212       | 0.1212          | 0.1212   |
| 1.9078        | 2.0   | 58   | 1.9066          | 0.0454   | 0.1818   | 0.0602      | 0.0272          | 0.1818          | 0.0361             | 0.1371       | 0.1818       | 0.1818          | 0.1818   |
| 1.9276        | 3.0   | 87   | 1.8808          | 0.0696   | 0.1818   | 0.0968      | 0.0492          | 0.1818          | 0.0682             | 0.1295       | 0.1818       | 0.1818          | 0.1818   |
| 1.8373        | 4.0   | 116  | 1.8696          | 0.0485   | 0.2045   | 0.0695      | 0.0292          | 0.2045          | 0.0418             | 0.1429       | 0.2045       | 0.2045          | 0.2045   |
| 1.8152        | 5.0   | 145  | 1.8490          | 0.1339   | 0.2576   | 0.1745      | 0.1298          | 0.2576          | 0.1640             | 0.1944       | 0.2576       | 0.2576          | 0.2576   |
| 1.8488        | 6.0   | 174  | 1.8281          | 0.1379   | 0.2727   | 0.1817      | 0.1512          | 0.2727          | 0.1891             | 0.1997       | 0.2727       | 0.2727          | 0.2727   |
| 1.7626        | 7.0   | 203  | 1.7917          | 0.2271   | 0.3333   | 0.2718      | 0.1922          | 0.3333          | 0.2298             | 0.2783       | 0.3333       | 0.3333          | 0.3333   |
| 1.7169        | 8.0   | 232  | 1.7478          | 0.2887   | 0.4242   | 0.3465      | 0.2706          | 0.4242          | 0.3154             | 0.3426       | 0.4242       | 0.4242          | 0.4242   |
| 1.5364        | 9.0   | 261  | 1.7098          | 0.2835   | 0.4091   | 0.3409      | 0.2720          | 0.4091          | 0.3245             | 0.3324       | 0.4091       | 0.4091          | 0.4091   |
| 1.7373        | 10.0  | 290  | 1.6765          | 0.2906   | 0.4167   | 0.3463      | 0.2726          | 0.4167          | 0.3157             | 0.3386       | 0.4167       | 0.4167          | 0.4167   |
| 1.5345        | 11.0  | 319  | 1.6423          | 0.2805   | 0.3939   | 0.3342      | 0.3728          | 0.3939          | 0.4258             | 0.3275       | 0.3939       | 0.3939          | 0.3939   |
| 1.6421        | 12.0  | 348  | 1.6103          | 0.3324   | 0.4697   | 0.3978      | 0.4583          | 0.4697          | 0.5178             | 0.3760       | 0.4697       | 0.4697          | 0.4697   |
| 1.5266        | 13.0  | 377  | 1.5835          | 0.3171   | 0.4621   | 0.3822      | 0.2917          | 0.4621          | 0.3483             | 0.3748       | 0.4621       | 0.4621          | 0.4621   |
| 1.5182        | 14.0  | 406  | 1.5633          | 0.3133   | 0.4242   | 0.3680      | 0.3634          | 0.4242          | 0.4009             | 0.3568       | 0.4242       | 0.4242          | 0.4242   |
| 1.5341        | 15.0  | 435  | 1.5528          | 0.3015   | 0.4167   | 0.3585      | 0.3109          | 0.4167          | 0.3638             | 0.3499       | 0.4167       | 0.4167          | 0.4167   |
| 1.3961        | 16.0  | 464  | 1.5273          | 0.3449   | 0.4545   | 0.3991      | 0.4329          | 0.4545          | 0.4704             | 0.3839       | 0.4545       | 0.4545          | 0.4545   |
| 1.3601        | 17.0  | 493  | 1.4971          | 0.3670   | 0.5      | 0.4357      | 0.5047          | 0.5             | 0.5382             | 0.4078       | 0.5          | 0.5             | 0.5      |
| 1.2535        | 18.0  | 522  | 1.5006          | 0.3511   | 0.4621   | 0.4138      | 0.4778          | 0.4621          | 0.5101             | 0.3872       | 0.4621       | 0.4621          | 0.4621   |
| 1.2375        | 19.0  | 551  | 1.4659          | 0.3655   | 0.4924   | 0.4345      | 0.4298          | 0.4924          | 0.4797             | 0.4020       | 0.4924       | 0.4924          | 0.4924   |
| 1.2141        | 20.0  | 580  | 1.4407          | 0.3914   | 0.5076   | 0.4565      | 0.4650          | 0.5076          | 0.5087             | 0.4217       | 0.5076       | 0.5076          | 0.5076   |
| 1.2831        | 21.0  | 609  | 1.4454          | 0.3965   | 0.5152   | 0.4645      | 0.4801          | 0.5152          | 0.5265             | 0.4214       | 0.5152       | 0.5152          | 0.5152   |
| 1.1543        | 22.0  | 638  | 1.4167          | 0.4285   | 0.5455   | 0.4997      | 0.4781          | 0.5455          | 0.5309             | 0.4521       | 0.5455       | 0.5455          | 0.5455   |
| 1.4079        | 23.0  | 667  | 1.4465          | 0.3675   | 0.4621   | 0.4269      | 0.4187          | 0.4621          | 0.4676             | 0.3929       | 0.4621       | 0.4621          | 0.4621   |
| 1.0619        | 24.0  | 696  | 1.4249          | 0.4092   | 0.5076   | 0.4724      | 0.4659          | 0.5076          | 0.5180             | 0.4336       | 0.5076       | 0.5076          | 0.5076   |
| 1.1059        | 25.0  | 725  | 1.3834          | 0.4356   | 0.5530   | 0.5061      | 0.5025          | 0.5530          | 0.5491             | 0.4594       | 0.5530       | 0.5530          | 0.5530   |
| 1.192         | 26.0  | 754  | 1.3784          | 0.4286   | 0.5379   | 0.4893      | 0.4566          | 0.5379          | 0.4969             | 0.4544       | 0.5379       | 0.5379          | 0.5379   |
| 1.21          | 27.0  | 783  | 1.3874          | 0.4409   | 0.5379   | 0.5060      | 0.4709          | 0.5379          | 0.5258             | 0.4616       | 0.5379       | 0.5379          | 0.5379   |
| 1.0901        | 28.0  | 812  | 1.3621          | 0.4402   | 0.5379   | 0.5074      | 0.4635          | 0.5379          | 0.5204             | 0.4557       | 0.5379       | 0.5379          | 0.5379   |
| 1.1254        | 29.0  | 841  | 1.3714          | 0.4265   | 0.5227   | 0.4873      | 0.4492          | 0.5227          | 0.4984             | 0.4449       | 0.5227       | 0.5227          | 0.5227   |
| 0.9345        | 30.0  | 870  | 1.3525          | 0.4425   | 0.5379   | 0.5074      | 0.4736          | 0.5379          | 0.5264             | 0.4557       | 0.5379       | 0.5379          | 0.5379   |
| 1.2036        | 31.0  | 899  | 1.3592          | 0.4363   | 0.5379   | 0.5020      | 0.4869          | 0.5379          | 0.5368             | 0.4533       | 0.5379       | 0.5379          | 0.5379   |
| 1.036         | 32.0  | 928  | 1.3362          | 0.4451   | 0.5455   | 0.5109      | 0.4673          | 0.5455          | 0.5226             | 0.4637       | 0.5455       | 0.5455          | 0.5455   |
| 0.9979        | 33.0  | 957  | 1.3492          | 0.4454   | 0.5455   | 0.5134      | 0.4808          | 0.5455          | 0.5358             | 0.4620       | 0.5455       | 0.5455          | 0.5455   |
| 0.8353        | 34.0  | 986  | 1.3402          | 0.4635   | 0.5606   | 0.5301      | 0.4659          | 0.5606          | 0.5268             | 0.4854       | 0.5606       | 0.5606          | 0.5606   |
| 0.9384        | 35.0  | 1015 | 1.3414          | 0.4408   | 0.5455   | 0.5088      | 0.4664          | 0.5455          | 0.5237             | 0.4602       | 0.5455       | 0.5455          | 0.5455   |
| 0.996         | 36.0  | 1044 | 1.3405          | 0.4559   | 0.5530   | 0.5235      | 0.4795          | 0.5530          | 0.5377             | 0.4715       | 0.5530       | 0.5530          | 0.5530   |
| 0.9613        | 37.0  | 1073 | 1.3357          | 0.4847   | 0.5833   | 0.5535      | 0.5011          | 0.5833          | 0.5612             | 0.5020       | 0.5833       | 0.5833          | 0.5833   |
| 0.8507        | 38.0  | 1102 | 1.3347          | 0.4760   | 0.5758   | 0.5454      | 0.4897          | 0.5758          | 0.5510             | 0.4940       | 0.5758       | 0.5758          | 0.5758   |
| 1.1563        | 39.0  | 1131 | 1.3396          | 0.4553   | 0.5530   | 0.5250      | 0.4608          | 0.5530          | 0.5234             | 0.4735       | 0.5530       | 0.5530          | 0.5530   |
| 0.9681        | 40.0  | 1160 | 1.3371          | 0.4703   | 0.5682   | 0.5396      | 0.4816          | 0.5682          | 0.5445             | 0.4887       | 0.5682       | 0.5682          | 0.5682   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- 0 1 2 3 4 5