Vit Facial Expression Recognition

🚀 vit-Facial-Expression-Recognition

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the FER 2013, MMI Facial Expression Database, and AffectNet dataset datasets. It achieves excellent performance on the evaluation set:

• Loss: 0.4503
• Accuracy: 0.8434

🚀 Quick Start

This model is ready to use for facial emotion recognition tasks. You can load it and start making predictions based on your needs.

✨ Features

• Fine - tuned Model: Based on the pre - trained google/vit-base-patch16-224-in21k, it is fine - tuned on multiple facial expression datasets.
• High Performance: Achieves high accuracy and low loss on the evaluation set.
• Multi - dataset Training: Trained on diverse datasets, enhancing generalization ability.

📚 Documentation

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, MMI facial Expression, and AffectNet 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.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e - 05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9, 0.999) and epsilon = 1e - 08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 1000
• num_epochs: 3

Training results

Framework versions

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