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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: Facial Expression Recognition
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8571428571428571
---
<!-- 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. -->
# Vision Transformer (ViT) for Facial Expression Recognition Model Card
## Model Overview
- **Model Name:** [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition)
- **Task:** Facial Expression/Emotion Recognition
- **Datasets:** [FER2013](https://www.kaggle.com/datasets/msambare/fer2013), [MMI Facial Expression Database](https://mmifacedb.eu)
- **Model Architecture:** [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)
- **Finetuned from model:** [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
- Loss: 0.4353
- Accuracy: 0.8571
## 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 consists 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
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