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# vit-base-cifar10

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0803
- Accuracy: 0.9773

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1043        | 0.0457 | 100  | 0.2855          | 0.919    |
| 0.2671        | 0.0914 | 200  | 0.3650          | 0.9015   |
| 0.2935        | 0.1371 | 300  | 0.3167          | 0.9067   |
| 0.27          | 0.1828 | 400  | 0.3518          | 0.8922   |
| 0.3634        | 0.2285 | 500  | 0.3660          | 0.8953   |
| 0.2559        | 0.2742 | 600  | 0.3964          | 0.8901   |
| 0.197         | 0.3199 | 700  | 0.2481          | 0.9253   |
| 0.2594        | 0.3656 | 800  | 0.2486          | 0.923    |
| 0.4545        | 0.4113 | 900  | 0.3271          | 0.9      |
| 0.1243        | 0.4570 | 1000 | 0.2448          | 0.9269   |
| 0.3593        | 0.5027 | 1100 | 0.2118          | 0.9354   |
| 0.1375        | 0.5484 | 1200 | 0.2205          | 0.9349   |
| 0.1521        | 0.5941 | 1300 | 0.2009          | 0.9376   |
| 0.1237        | 0.6399 | 1400 | 0.1803          | 0.9445   |
| 0.2214        | 0.6856 | 1500 | 0.2026          | 0.9395   |
| 0.1324        | 0.7313 | 1600 | 0.1635          | 0.9493   |
| 0.1864        | 0.7770 | 1700 | 0.1672          | 0.9493   |
| 0.128         | 0.8227 | 1800 | 0.2015          | 0.9409   |
| 0.121         | 0.8684 | 1900 | 0.1753          | 0.9451   |
| 0.1918        | 0.9141 | 2000 | 0.1370          | 0.9588   |
| 0.1658        | 0.9598 | 2100 | 0.1543          | 0.9535   |
| 0.1088        | 1.0055 | 2200 | 0.1361          | 0.9577   |
| 0.0916        | 1.0512 | 2300 | 0.1393          | 0.9597   |
| 0.005         | 1.0969 | 2400 | 0.1295          | 0.9621   |
| 0.0294        | 1.1426 | 2500 | 0.1327          | 0.9639   |
| 0.0939        | 1.1883 | 2600 | 0.1409          | 0.9621   |
| 0.0756        | 1.2340 | 2700 | 0.1202          | 0.9682   |
| 0.0466        | 1.2797 | 2800 | 0.1274          | 0.964    |
| 0.0565        | 1.3254 | 2900 | 0.1250          | 0.9663   |
| 0.0609        | 1.3711 | 3000 | 0.1299          | 0.9657   |
| 0.0201        | 1.4168 | 3100 | 0.1203          | 0.9685   |
| 0.0258        | 1.4625 | 3200 | 0.1166          | 0.9693   |
| 0.0913        | 1.5082 | 3300 | 0.1009          | 0.9736   |
| 0.0235        | 1.5539 | 3400 | 0.0964          | 0.9732   |
| 0.0089        | 1.5996 | 3500 | 0.0966          | 0.9747   |
| 0.0455        | 1.6453 | 3600 | 0.0963          | 0.9748   |
| 0.0271        | 1.6910 | 3700 | 0.0874          | 0.9763   |
| 0.0407        | 1.7367 | 3800 | 0.0898          | 0.9761   |
| 0.1095        | 1.7824 | 3900 | 0.0849          | 0.976    |
| 0.0327        | 1.8282 | 4000 | 0.0926          | 0.9745   |
| 0.0427        | 1.8739 | 4100 | 0.0811          | 0.9769   |
| 0.003         | 1.9196 | 4200 | 0.0821          | 0.9761   |
| 0.0182        | 1.9653 | 4300 | 0.0803          | 0.9773   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

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