Main script to run is test_plaus_or_faith.py. Plausibility Guided Training implemented (run train_pgt.py). Accuracy of training not yet tested.
This project is a collaborative effort between the Machine Learning, AI, and Virtual Reality Center at Rowan University and the Picatinny Arsenal. We, the AI team at Rowan, have been tasked with developing a drone detection algorithm that enhances the survivability of turret gunners.
Our solution leverages a forked version of YOLOv7, which we have modified to include a special metric for small object detection known as the Normalized Wasserstein Distance. This metric improves the model's ability to detect small objects, a crucial requirement for our simulation environment.
This guide will walk you through the steps to create a new virtual environment, install the required dependencies specified in the requirements.txt
file.
Make sure you have the following installed on your system:
- Python 3.6 or later
- Git
- W&B account (sign up here)
-
Clone the repository to your local machine.
git clone https://github.com/naddeok96/yolov7_mavrc
-
Navigate to the project directory.
cd yolov7_mavrc
-
Create a new virtual environment.
python3 -m venv venv_name
-
Activate the virtual environment.
source venv_name/bin/activate
- Install the dependencies specified in the
requirements.txt
file.pip3 install -r requirements.txt