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# vit-emotion-classifier

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3090
- Accuracy: 0.55

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4729        | 1.0   | 10   | 1.5748          | 0.4875   |
| 1.4484        | 2.0   | 20   | 1.5526          | 0.4875   |
| 1.4053        | 3.0   | 30   | 1.5228          | 0.4562   |
| 1.3492        | 4.0   | 40   | 1.4721          | 0.5      |
| 1.2664        | 5.0   | 50   | 1.4448          | 0.5125   |
| 1.2005        | 6.0   | 60   | 1.3783          | 0.5062   |
| 1.1231        | 7.0   | 70   | 1.3427          | 0.5375   |
| 1.0472        | 8.0   | 80   | 1.2859          | 0.5625   |
| 0.9852        | 9.0   | 90   | 1.2732          | 0.5813   |
| 0.8974        | 10.0  | 100  | 1.2220          | 0.575    |
| 0.8314        | 11.0  | 110  | 1.2782          | 0.5312   |
| 0.7964        | 12.0  | 120  | 1.2889          | 0.5437   |
| 0.6993        | 13.0  | 130  | 1.2989          | 0.5188   |
| 0.6915        | 14.0  | 140  | 1.3053          | 0.5375   |
| 0.608         | 15.0  | 150  | 1.2563          | 0.5875   |
| 0.5416        | 16.0  | 160  | 1.2473          | 0.5563   |
| 0.5202        | 17.0  | 170  | 1.2753          | 0.5625   |
| 0.5047        | 18.0  | 180  | 1.2791          | 0.5563   |
| 0.4779        | 19.0  | 190  | 1.3142          | 0.5437   |
| 0.4569        | 20.0  | 200  | 1.2743          | 0.5813   |
| 0.4313        | 21.0  | 210  | 1.2727          | 0.5312   |
| 0.4536        | 22.0  | 220  | 1.2514          | 0.5938   |
| 0.4166        | 23.0  | 230  | 1.3260          | 0.5312   |
| 0.3673        | 24.0  | 240  | 1.2950          | 0.55     |
| 0.3544        | 25.0  | 250  | 1.2268          | 0.5875   |
| 0.3568        | 26.0  | 260  | 1.3874          | 0.4875   |
| 0.3509        | 27.0  | 270  | 1.3735          | 0.525    |
| 0.3711        | 28.0  | 280  | 1.2886          | 0.5375   |
| 0.3555        | 29.0  | 290  | 1.3152          | 0.5375   |
| 0.3068        | 30.0  | 300  | 1.3927          | 0.5375   |
| 0.3007        | 31.0  | 310  | 1.4131          | 0.5188   |
| 0.3062        | 32.0  | 320  | 1.3256          | 0.575    |
| 0.3114        | 33.0  | 330  | 1.3714          | 0.5      |
| 0.279         | 34.0  | 340  | 1.4198          | 0.5188   |
| 0.2888        | 35.0  | 350  | 1.5321          | 0.475    |
| 0.2647        | 36.0  | 360  | 1.4342          | 0.5062   |
| 0.2574        | 37.0  | 370  | 1.4149          | 0.5563   |
| 0.2539        | 38.0  | 380  | 1.4286          | 0.5125   |
| 0.2566        | 39.0  | 390  | 1.4805          | 0.5125   |
| 0.2298        | 40.0  | 400  | 1.3820          | 0.4875   |
| 0.2236        | 41.0  | 410  | 1.3683          | 0.5437   |
| 0.2201        | 42.0  | 420  | 1.3332          | 0.5687   |
| 0.2696        | 43.0  | 430  | 1.4725          | 0.5188   |
| 0.2319        | 44.0  | 440  | 1.3926          | 0.5375   |
| 0.2269        | 45.0  | 450  | 1.3477          | 0.5563   |
| 0.2201        | 46.0  | 460  | 1.4054          | 0.5563   |
| 0.2114        | 47.0  | 470  | 1.3308          | 0.55     |
| 0.2319        | 48.0  | 480  | 1.3353          | 0.5625   |
| 0.2177        | 49.0  | 490  | 1.3019          | 0.5437   |
| 0.2042        | 50.0  | 500  | 1.3089          | 0.5875   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

anger contempt disgust fear happy neutral sad surprise