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# vit-cifar10-fine-tuned

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0603

## Model description

Made few prediction tests on following classes during self study.

* airplane
* automobile
* bird
* cat
* deer
* dog
* frog
* horse
* ship
* truck


![image/png](https://cdn-uploads.huggingface.co/production/uploads/6775482fceb0e3e5e703e7e2/RzQVKd26Bu6ewiiqht7hp.png)


## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1156        | 0.16   | 500  | 0.1052          |
| 0.0768        | 0.32   | 1000 | 0.0869          |
| 0.0552        | 0.48   | 1500 | 0.0761          |
| 0.0533        | 0.64   | 2000 | 0.0614          |
| 0.0441        | 0.8    | 2500 | 0.0712          |
| 0.0567        | 0.96   | 3000 | 0.0699          |
| 0.0101        | 1.12   | 3500 | 0.0583          |
| 0.0075        | 1.28   | 4000 | 0.0630          |
| 0.0091        | 1.44   | 4500 | 0.0617          |
| 0.0122        | 1.6    | 5000 | 0.0611          |
| 0.0153        | 1.76   | 5500 | 0.0589          |
| 0.0173        | 1.92   | 6000 | 0.0716          |
| 0.0018        | 2.08   | 6500 | 0.0586          |
| 0.0062        | 2.24   | 7000 | 0.0610          |
| 0.0061        | 2.4    | 7500 | 0.0578          |
| 0.0005        | 2.56   | 8000 | 0.0555          |
| 0.0006        | 2.7200 | 8500 | 0.0591          |
| 0.0003        | 2.88   | 9000 | 0.0603          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0

airplane automobile bird cat deer dog frog horse ship truck