
<!-- 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. -->

# ViTForImageClassification

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 [CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Accuracy: 0.9678

## Model description

[A detailed description of model architecture can be found here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/modeling_vit.py#L756)

## Training and evaluation data

[CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom)

## Training procedure
Straightforward tuning of all model's parameters.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2995        | 0.27  | 100  | 0.3419          | 0.9108   |
| 0.2289        | 0.53  | 200  | 0.2482          | 0.9288   |
| 0.1811        | 0.8   | 300  | 0.2139          | 0.9357   |
| 0.0797        | 1.07  | 400  | 0.1813          | 0.946    |
| 0.1128        | 1.33  | 500  | 0.1741          | 0.9452   |
| 0.086         | 1.6   | 600  | 0.1659          | 0.9513   |
| 0.0815        | 1.87  | 700  | 0.1468          | 0.9547   |
| 0.048         | 2.13  | 800  | 0.1393          | 0.9592   |
| 0.021         | 2.4   | 900  | 0.1399          | 0.9603   |
| 0.0271        | 2.67  | 1000 | 0.1334          | 0.9642   |
| 0.0231        | 2.93  | 1100 | 0.1228          | 0.9658   |
| 0.0101        | 3.2   | 1200 | 0.1229          | 0.9673   |
| 0.0041        | 3.47  | 1300 | 0.1189          | 0.9675   |
| 0.0043        | 3.73  | 1400 | 0.1165          | 0.9683   |
| 0.0067        | 4.0   | 1500 | 0.1145          | 0.9697   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1

airplane automobile bird cat deer dog frog horse ship truck