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 the following results on the evaluation set:
- Loss: 0.4503
- Accuracy: 0.8434
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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|
| 1.3548 | 0.17 | 100 | 0.8024 | 0.7418 |
| 1.047 | 0.34 | 200 | 0.6823 | 0.7653 |
| 0.9398 | 0.51 | 300 | 0.6264 | 0.7827 |
| 0.8618 | 0.67 | 400 | 0.5857 | 0.7973 |
| 0.8363 | 0.84 | 500 | 0.5532 | 0.8104 |
| 0.8018 | 1.01 | 600 | 0.5279 | 0.8196 |
| 0.7567 | 1.18 | 700 | 0.5110 | 0.8248 |
| 0.7521 | 1.35 | 800 | 0.5080 | 0.8259 |
| 0.741 | 1.52 | 900 | 0.5002 | 0.8271 |
| 0.7229 | 1.69 | 1000 | 0.4967 | 0.8263 |
| 0.7157 | 1.85 | 1100 | 0.4876 | 0.8326 |
| 0.6868 | 2.02 | 1200 | 0.4836 | 0.8342 |
| 0.6605 | 2.19 | 1300 | 0.4711 | 0.8384 |
| 0.6449 | 2.36 | 1400 | 0.4608 | 0.8406 |
| 0.6085 | 2.53 | 1500 | 0.4503 | 0.8434 |
| 0.6178 | 2.7 | 1600 | 0.4434 | 0.8478 |
| 0.6166 | 2.87 | 1700 | 0.4420 | 0.8486 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
