Project repository for the group project in NTNU course TDT4173 - Machine Learning, Fall 2020. WoodNet¹ is a Convolutional Neural Network trained for facial recognition, for use in entry access control. See this video explaining the project.
Made by Lars Ankile, Morgan Heggland and Kjartan Krange.
¹The network is named after Fleetwood Mac, as one of the authors recently discovered their enchanting tunes².
²The other authors have been acquainted with Fleetwood Mac for a while.
The code used to process the data and train the models is run in notebooks using virtual machines and require no setup other than access to a virtual machine (e.g. Google Colab or Google Cloud VMs). The notebooks can be found in notebooks, containing both data pre-processing (data-extraction.ipynb) and model implementation and training (Train_plot_and_save_224px_color.ipynb).
To run the accompanying live inference demo, follow the steps below:
- Clone the repo using
git clone - Enter the project directory with
cd woodnet - Install a virtual environment for the project:
python3 -m venv venv - Activate virtual environment with
source venv/bin/activate - Install depenencies needed to run the demo with
pip install -r src/demo/requirements.txt - Start the demo by running
python3 src/demo/demo.py(requires a camera connected to the computer)
Note: the demo runs on CPU and thus run with a rather limited framerate. It is possible to run on GPU by editing the code where the facial recognition model is loaded in recognizer.py. This requires a GPU + CUDA installed and pyTorch configured for GPU, and is not tested.
PS: the demo attempts to classify faces present in the webcam according to one of the clases Lars, Morgan, Kjartanand Other. In a perfect world, this would require one of the authors to be present when you run the demo in order for it to predict anything other than Other. However, as the models are not perfect, this is not always the case.
The data used to train the models in this repository (beyond pre-trained models used for transfer learning) have been collected by the authors and is stored in Google Cloud Storage:
- Raw images (5.2 GB)
- Dataset cropped using face detection (3.1 GB)
- Dataset cropped around center of image (3.3 GB)
models- Storage of built, persisted models used in the project.model_1- ...
notebooks- Notebooks used for testing, data exploration, data processing and training.src- The core source code for the project.models- Source code defining the models.demo- Directory containing implementation of a live classification demo using a connected webcam feed.visualization- Code for visualization for evaluation of models.