Computer vision web application, built to predict the age, race, and gender of all individuals present in an image
- Achieves an average 85% accuracy across all three dependent variables
- CNN model built and trained in PyTorch
- Developed with a Flask Python backend and bootstrap frontend
Testing it out on myself!
This facial classifier runs on Python 3.7
Start off by cloning the repo:
git clone https://github.com/danielzgsilva
Navigate to the project's root and install dependencies like so:
pip install -r requirements.txt
Run the app with:
python app.py
The project will then serve locally on port 5555:
http://localhost:5555/
The underlying Convolutional Neural Network uses a pretrained Squeeze and Excitation Network (SENet), trained on VGGFace2
- Cao, Qiong, et al. "Vggface2: A dataset for recognising faces across pose and age." Automatic Face & Gesture Recognition (FG 2018), 2018 13th IEEE International Conference on. IEEE, 2018.
The model was fine tuned for classification of tightly cropped facial images using the UTKFace dataset
- Deploying the app through Heroku
- Combatting learned biases due to imbalanced datasets
- Adding the ability to take a picture through the app, rather than requiring an image upload
- Increasing performance in poor lighting