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# vit-base-patch16-224

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3122
- F1 Macro: 0.5397
- F1 Micro: 0.6212
- F1 Weighted: 0.6077
- Precision Macro: 0.5343
- Precision Micro: 0.6212
- Precision Weighted: 0.6084
- Recall Macro: 0.5571
- Recall Micro: 0.6212
- Recall Weighted: 0.6212
- Accuracy: 0.6212

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9037        | 1.0   | 29   | 1.8618          | 0.1250   | 0.2197   | 0.1592      | 0.1401          | 0.2197          | 0.1692             | 0.1674       | 0.2197       | 0.2197          | 0.2197   |
| 1.6981        | 2.0   | 58   | 1.8760          | 0.1537   | 0.2424   | 0.1896      | 0.2152          | 0.2424          | 0.2787             | 0.2068       | 0.2424       | 0.2424          | 0.2424   |
| 1.7426        | 3.0   | 87   | 1.6971          | 0.2272   | 0.3333   | 0.2622      | 0.1959          | 0.3333          | 0.2233             | 0.2846       | 0.3333       | 0.3333          | 0.3333   |
| 1.1847        | 4.0   | 116  | 1.5082          | 0.3360   | 0.4242   | 0.3911      | 0.3925          | 0.4242          | 0.4281             | 0.3553       | 0.4242       | 0.4242          | 0.4242   |
| 1.3906        | 5.0   | 145  | 1.4063          | 0.3152   | 0.4621   | 0.3815      | 0.2727          | 0.4621          | 0.3279             | 0.3785       | 0.4621       | 0.4621          | 0.4621   |
| 1.5575        | 6.0   | 174  | 1.3833          | 0.4414   | 0.4621   | 0.4526      | 0.4850          | 0.4621          | 0.4941             | 0.4402       | 0.4621       | 0.4621          | 0.4621   |
| 1.1063        | 7.0   | 203  | 1.2431          | 0.4750   | 0.5833   | 0.5453      | 0.5898          | 0.5833          | 0.6329             | 0.4890       | 0.5833       | 0.5833          | 0.5833   |
| 1.1503        | 8.0   | 232  | 1.3635          | 0.4036   | 0.4924   | 0.4586      | 0.4145          | 0.4924          | 0.4762             | 0.4436       | 0.4924       | 0.4924          | 0.4924   |
| 0.5124        | 9.0   | 261  | 1.1603          | 0.5463   | 0.6288   | 0.6136      | 0.5488          | 0.6288          | 0.6113             | 0.5551       | 0.6288       | 0.6288          | 0.6288   |
| 0.6648        | 10.0  | 290  | 1.4136          | 0.4184   | 0.5      | 0.4713      | 0.4713          | 0.5             | 0.5275             | 0.4413       | 0.5          | 0.5             | 0.5      |
| 0.2917        | 11.0  | 319  | 1.2004          | 0.5155   | 0.6061   | 0.5892      | 0.5180          | 0.6061          | 0.5882             | 0.5268       | 0.6061       | 0.6061          | 0.6061   |
| 0.4962        | 12.0  | 348  | 1.3730          | 0.4970   | 0.5682   | 0.5671      | 0.5094          | 0.5682          | 0.5909             | 0.5109       | 0.5682       | 0.5682          | 0.5682   |
| 0.5723        | 13.0  | 377  | 1.3377          | 0.5705   | 0.6136   | 0.6077      | 0.7050          | 0.6136          | 0.6879             | 0.5756       | 0.6136       | 0.6136          | 0.6136   |
| 0.4589        | 14.0  | 406  | 1.3717          | 0.5648   | 0.6136   | 0.6094      | 0.6239          | 0.6136          | 0.6458             | 0.5609       | 0.6136       | 0.6136          | 0.6136   |
| 0.2544        | 15.0  | 435  | 1.4129          | 0.5086   | 0.5985   | 0.5793      | 0.5140          | 0.5985          | 0.5772             | 0.5187       | 0.5985       | 0.5985          | 0.5985   |
| 0.3179        | 16.0  | 464  | 1.3589          | 0.5882   | 0.6439   | 0.6347      | 0.6912          | 0.6439          | 0.6603             | 0.5777       | 0.6439       | 0.6439          | 0.6439   |
| 0.1304        | 17.0  | 493  | 1.5604          | 0.5010   | 0.5758   | 0.5606      | 0.5123          | 0.5758          | 0.5669             | 0.5076       | 0.5758       | 0.5758          | 0.5758   |
| 0.0887        | 18.0  | 522  | 1.6231          | 0.5091   | 0.6061   | 0.5800      | 0.5344          | 0.6061          | 0.5917             | 0.5190       | 0.6061       | 0.6061          | 0.6061   |
| 0.0254        | 19.0  | 551  | 1.6095          | 0.5625   | 0.6136   | 0.6070      | 0.6642          | 0.6136          | 0.6353             | 0.5520       | 0.6136       | 0.6136          | 0.6136   |
| 0.0908        | 20.0  | 580  | 1.6941          | 0.5270   | 0.6136   | 0.5962      | 0.5331          | 0.6136          | 0.6004             | 0.5381       | 0.6136       | 0.6136          | 0.6136   |
| 0.0913        | 21.0  | 609  | 1.6917          | 0.5537   | 0.6136   | 0.6018      | 0.5909          | 0.6136          | 0.6169             | 0.5579       | 0.6136       | 0.6136          | 0.6136   |
| 0.015         | 22.0  | 638  | 1.8274          | 0.4866   | 0.5682   | 0.5512      | 0.4855          | 0.5682          | 0.5477             | 0.5003       | 0.5682       | 0.5682          | 0.5682   |
| 0.0156        | 23.0  | 667  | 1.7322          | 0.5772   | 0.6439   | 0.6233      | 0.6870          | 0.6439          | 0.6598             | 0.5802       | 0.6439       | 0.6439          | 0.6439   |
| 0.0275        | 24.0  | 696  | 1.6262          | 0.5293   | 0.6212   | 0.6006      | 0.5274          | 0.6212          | 0.5913             | 0.5422       | 0.6212       | 0.6212          | 0.6212   |
| 0.0034        | 25.0  | 725  | 1.7278          | 0.5680   | 0.6591   | 0.6409      | 0.5674          | 0.6591          | 0.6333             | 0.5786       | 0.6591       | 0.6591          | 0.6591   |
| 0.0021        | 26.0  | 754  | 1.7111          | 0.5542   | 0.6439   | 0.6250      | 0.5506          | 0.6439          | 0.6148             | 0.5657       | 0.6439       | 0.6439          | 0.6439   |
| 0.0021        | 27.0  | 783  | 1.7412          | 0.5556   | 0.6439   | 0.6257      | 0.5507          | 0.6439          | 0.6163             | 0.5684       | 0.6439       | 0.6439          | 0.6439   |
| 0.0079        | 28.0  | 812  | 1.8651          | 0.5506   | 0.6364   | 0.6176      | 0.5427          | 0.6364          | 0.6078             | 0.5670       | 0.6364       | 0.6364          | 0.6364   |
| 0.0018        | 29.0  | 841  | 1.8016          | 0.5508   | 0.6364   | 0.6184      | 0.5425          | 0.6364          | 0.6074             | 0.5654       | 0.6364       | 0.6364          | 0.6364   |
| 0.0068        | 30.0  | 870  | 1.7936          | 0.5510   | 0.6364   | 0.6182      | 0.5436          | 0.6364          | 0.6073             | 0.5650       | 0.6364       | 0.6364          | 0.6364   |


### 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