---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_face_image_classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.55
---

<!-- 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. -->

# emotion_face_image_classification

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: 1.2110
- 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0717        | 1.0   | 10   | 2.0593          | 0.2062   |
| 2.005         | 2.0   | 20   | 1.9999          | 0.2625   |
| 1.9169        | 3.0   | 30   | 1.8931          | 0.35     |
| 1.7635        | 4.0   | 40   | 1.7616          | 0.4062   |
| 1.6614        | 5.0   | 50   | 1.6452          | 0.4562   |
| 1.6182        | 6.0   | 60   | 1.5661          | 0.4125   |
| 1.5434        | 7.0   | 70   | 1.5183          | 0.4125   |
| 1.46          | 8.0   | 80   | 1.4781          | 0.4875   |
| 1.4564        | 9.0   | 90   | 1.3939          | 0.5125   |
| 1.2966        | 10.0  | 100  | 1.3800          | 0.4562   |
| 1.3732        | 11.0  | 110  | 1.3557          | 0.475    |
| 1.2907        | 12.0  | 120  | 1.3473          | 0.5      |
| 1.2875        | 13.0  | 130  | 1.3416          | 0.5312   |
| 1.2743        | 14.0  | 140  | 1.2964          | 0.4875   |
| 1.1249        | 15.0  | 150  | 1.2385          | 0.525    |
| 1.0963        | 16.0  | 160  | 1.2775          | 0.5062   |
| 1.0261        | 17.0  | 170  | 1.2751          | 0.5125   |
| 0.9298        | 18.0  | 180  | 1.2318          | 0.525    |
| 1.0668        | 19.0  | 190  | 1.2520          | 0.5437   |
| 0.9933        | 20.0  | 200  | 1.2512          | 0.525    |
| 1.1069        | 21.0  | 210  | 1.3016          | 0.5      |
| 1.0279        | 22.0  | 220  | 1.3279          | 0.475    |
| 0.967         | 23.0  | 230  | 1.2481          | 0.5      |
| 0.8115        | 24.0  | 240  | 1.1791          | 0.5563   |
| 0.7912        | 25.0  | 250  | 1.2336          | 0.55     |
| 0.9294        | 26.0  | 260  | 1.1759          | 0.5813   |
| 0.8936        | 27.0  | 270  | 1.1685          | 0.6      |
| 0.7706        | 28.0  | 280  | 1.2403          | 0.5312   |
| 0.7694        | 29.0  | 290  | 1.2479          | 0.5687   |
| 0.7265        | 30.0  | 300  | 1.2000          | 0.5625   |
| 0.6781        | 31.0  | 310  | 1.1856          | 0.55     |
| 0.6676        | 32.0  | 320  | 1.2661          | 0.5437   |
| 0.7254        | 33.0  | 330  | 1.1986          | 0.5437   |
| 0.7396        | 34.0  | 340  | 1.1497          | 0.575    |
| 0.5532        | 35.0  | 350  | 1.2796          | 0.5062   |
| 0.622         | 36.0  | 360  | 1.2749          | 0.5125   |
| 0.6958        | 37.0  | 370  | 1.2034          | 0.5687   |
| 0.6102        | 38.0  | 380  | 1.2576          | 0.5188   |
| 0.6161        | 39.0  | 390  | 1.2635          | 0.5062   |
| 0.6927        | 40.0  | 400  | 1.1535          | 0.5437   |
| 0.549         | 41.0  | 410  | 1.1405          | 0.6      |
| 0.6668        | 42.0  | 420  | 1.2683          | 0.5312   |
| 0.5144        | 43.0  | 430  | 1.2249          | 0.6      |
| 0.6703        | 44.0  | 440  | 1.2297          | 0.5687   |
| 0.6383        | 45.0  | 450  | 1.1507          | 0.6062   |
| 0.5211        | 46.0  | 460  | 1.2914          | 0.4813   |
| 0.4743        | 47.0  | 470  | 1.2782          | 0.5125   |
| 0.553         | 48.0  | 480  | 1.2256          | 0.5375   |
| 0.6407        | 49.0  | 490  | 1.2149          | 0.5687   |
| 0.4195        | 50.0  | 500  | 1.2024          | 0.5625   |


### Framework versions

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