---
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: 1.2556
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 10   | 2.0561          | 0.1437   |
| No log        | 2.0   | 20   | 1.9946          | 0.325    |
| No log        | 3.0   | 30   | 1.9059          | 0.3563   |
| No log        | 4.0   | 40   | 1.7640          | 0.3937   |
| No log        | 5.0   | 50   | 1.6329          | 0.3812   |
| No log        | 6.0   | 60   | 1.5935          | 0.375    |
| No log        | 7.0   | 70   | 1.5206          | 0.4313   |
| No log        | 8.0   | 80   | 1.4580          | 0.4813   |
| No log        | 9.0   | 90   | 1.4422          | 0.4938   |
| No log        | 10.0  | 100  | 1.4297          | 0.4437   |
| No log        | 11.0  | 110  | 1.4020          | 0.5062   |
| No log        | 12.0  | 120  | 1.3344          | 0.5125   |
| No log        | 13.0  | 130  | 1.3541          | 0.5125   |
| No log        | 14.0  | 140  | 1.2990          | 0.5125   |
| No log        | 15.0  | 150  | 1.2482          | 0.5687   |
| No log        | 16.0  | 160  | 1.2726          | 0.4875   |
| No log        | 17.0  | 170  | 1.2523          | 0.5375   |
| No log        | 18.0  | 180  | 1.2857          | 0.4813   |
| No log        | 19.0  | 190  | 1.2476          | 0.5375   |
| No log        | 20.0  | 200  | 1.2991          | 0.4625   |
| No log        | 21.0  | 210  | 1.3108          | 0.5062   |
| No log        | 22.0  | 220  | 1.3052          | 0.5      |
| No log        | 23.0  | 230  | 1.2606          | 0.4813   |
| No log        | 24.0  | 240  | 1.1568          | 0.5687   |
| No log        | 25.0  | 250  | 1.2242          | 0.5437   |
| No log        | 26.0  | 260  | 1.2031          | 0.5875   |
| No log        | 27.0  | 270  | 1.1649          | 0.5625   |
| No log        | 28.0  | 280  | 1.3134          | 0.4938   |
| No log        | 29.0  | 290  | 1.2542          | 0.5062   |
| No log        | 30.0  | 300  | 1.1542          | 0.5687   |
| No log        | 31.0  | 310  | 1.1619          | 0.5625   |
| No log        | 32.0  | 320  | 1.2700          | 0.5      |
| No log        | 33.0  | 330  | 1.1943          | 0.55     |
| No log        | 34.0  | 340  | 1.1441          | 0.575    |
| No log        | 35.0  | 350  | 1.2405          | 0.5687   |
| No log        | 36.0  | 360  | 1.2228          | 0.5375   |
| No log        | 37.0  | 370  | 1.1598          | 0.6      |
| No log        | 38.0  | 380  | 1.1894          | 0.5687   |
| No log        | 39.0  | 390  | 1.2493          | 0.5188   |
| No log        | 40.0  | 400  | 1.1716          | 0.5687   |
| No log        | 41.0  | 410  | 1.1558          | 0.5813   |
| No log        | 42.0  | 420  | 1.2529          | 0.5312   |
| No log        | 43.0  | 430  | 1.2515          | 0.5625   |
| No log        | 44.0  | 440  | 1.2576          | 0.5188   |
| No log        | 45.0  | 450  | 1.1793          | 0.5687   |
| No log        | 46.0  | 460  | 1.2252          | 0.5625   |
| No log        | 47.0  | 470  | 1.1876          | 0.575    |
| No log        | 48.0  | 480  | 1.2288          | 0.5375   |
| No log        | 49.0  | 490  | 1.2623          | 0.5125   |
| 0.989         | 50.0  | 500  | 1.1603          | 0.5875   |


### Framework versions

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