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vit-Facial-Expression-Recognition

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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Facial Expression Recognition
    results:
      - task:
          name: Image Classification
          type: image-classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8571428571428571
pipeline_tag: image-classification

Vision Transformer (ViT) for Facial Expression Recognition Model Card

Model Overview

Model description

The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition.

It is trained on the FER2013 and MMI facial Expression datasets , which consist of facial images categorized into seven different emotions:

  • Angry
  • Disgust
  • Fear
  • Happy
  • Sad
  • Surprise
  • Neutral

Data Preprocessing

The input images are preprocessed before being fed into the model. The preprocessing steps include:

  • Resizing: Images are resized to the specified input size.
  • Normalization: Pixel values are normalized to a specific range.
  • Data Augmentation: Random transformations such as rotations, flips, and zooms are applied to augment the training dataset.

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Accuracy Validation Loss
0.7964 1.0 798 0.7271 0.7869
0.6567 2.0 1596 0.7380 0.7539
0.6842 3.0 2394 0.7837 0.6287
0.5242 4.0 3192 0.7839 0.6282
0.4321 5.0 3990 0.7823 0.6423
0.3129 6.0 4788 0.7838 0.6533
0.4245 7.0 5586 0.8542 0.4382
0.3806 8.0 6384 0.8531 0.4375
0.3112 9.0 7182 0.8557 0.4372
0.2692 10.0 7980 0.8571 0.4353

Framework versions

  • Transformers 4.36.0
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.15.0