
<!-- 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. -->

# finetuned-classficatin-fer2013

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 fer2013 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0586
- Accuracy: 0.6952

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5814        | 0.06  | 100  | 1.1124          | 0.6525   |
| 0.3856        | 0.11  | 200  | 1.1988          | 0.6428   |
| 0.4073        | 0.17  | 300  | 1.2319          | 0.6492   |
| 0.3408        | 0.22  | 400  | 1.1527          | 0.6581   |
| 0.4001        | 0.28  | 500  | 1.1861          | 0.6601   |
| 0.3973        | 0.33  | 600  | 1.1161          | 0.6637   |
| 0.3897        | 0.39  | 700  | 1.2955          | 0.6464   |
| 0.4689        | 0.45  | 800  | 1.1492          | 0.6578   |
| 0.4173        | 0.5   | 900  | 1.1538          | 0.6659   |
| 0.3238        | 0.56  | 1000 | 1.1742          | 0.6704   |
| 0.2774        | 0.61  | 1100 | 1.1426          | 0.6771   |
| 0.3948        | 0.67  | 1200 | 1.1533          | 0.6701   |
| 0.3258        | 0.72  | 1300 | 1.1405          | 0.6743   |
| 0.3816        | 0.78  | 1400 | 1.1101          | 0.6838   |
| 0.308         | 0.84  | 1500 | 1.1281          | 0.6871   |
| 0.4592        | 0.89  | 1600 | 1.0971          | 0.6938   |
| 0.3957        | 0.95  | 1700 | 1.0586          | 0.6952   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3

angry disgust fear happy sad surprise neutral