FERViT

This model is a fine-tuned version of motheecreator/vit-Facial-Expression-Recognition on the image_folder dataset.

It achieves the following results on the evaluation set:

• Loss: 0.2527
• Accuracy: 0.9205

Model description

FERViT is a Vision Transformer (ViT) model fine-tuned for facial expression recognition.

The associated research investigates facial expression recognition using Convolutional Neural Networks (CNNs) and Vision Transformers, comparing full-face images, facial landmarks, and hybrid representations across FER-2013, FER+, and RAF-DB.

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: 3e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 512
• 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

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5175	1.78	100	0.2538	0.9186

Framework versions

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

Citation

If you use this model in your research, please cite the associated work:

Casanova, D., Hoffmann, A. B. G., Candido Junior, A., Rodrigues, P. J., & de Paula Filho, P. L. (2026). Facial Expression Recognition with CNNs and Vision Transformers: Full Images, Landmarks, and a Hybrid Method on FER-2013, FER+ and RAF-DB. In VI International Conference on Optimization, Learning Algorithms and Applications (OL2A 2026): Book of Abstracts (p. 36). Instituto Politécnico de Bragança.

Publication: OL2A 2026 — Book of Abstracts

BibTeX

@inproceedings{casanova2026facial,
  author    = {Casanova, Daniel and
               Hoffmann, Alessandra B. G. and
               Candido Junior, Arnaldo and
               Rodrigues, Pedro Jo{\~a}o and
               de Paula Filho, Pedro Luiz},
  title     = {Facial Expression Recognition with {CNNs}
               and Vision Transformers: Full Images,
               Landmarks, and a Hybrid Method on
               {FER-2013}, {FER+} and {RAF-DB}},
  booktitle = {VI International Conference on Optimization,
               Learning Algorithms and Applications
               ({OL2A} 2026): Book of Abstracts},
  year      = {2026},
  pages     = {36},
  publisher = {Instituto Polit{\'e}cnico de Bragan{\c{c}}a},
  isbn      = {978-972-745-373-3},
  url       = {http://hdl.handle.net/10198/36787},
  note      = {Conference abstract}
}