|
--- |
|
license: apache-2.0 |
|
base_model: motheecreator/vit-Facial-Expression-Recognition |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: vit-Facial-Expression-Recognition |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# vit-Facial-Expression-Recognition |
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [FER 2013](https://www.kaggle.com/datasets/msambare/fer2013),[MMI Facial Expression Database](https://mmifacedb.eu/), and [AffectNet dataset](https://www.kaggle.com/datasets/noamsegal/affectnet-training-data) 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 |
|
|
