Chess-detection-using-YOLOv2

Screenshots of the output

This repository contains code for a object detector based on YOLO9000: Better, Faster, Stronger, implementeded using Deeplearning4J or DL4J. The code is based on codes of Eclipse Deeplearning4j training lab. The program uses a pretained Yolo-v2 Model from DL4J zoo package. Model was trained on 239 images.

Setup

Before running the code

Install Java

Download Java JDK here.
(Note: Use Java 8 for full support of DL4J operations)

Check the version of Java using:

java -version

Make sure that 64-Bit version of Java is installed.

Install IntelliJ IDEA Community Edition

Download and install IntelliJ IDEA.

Install Apache Maven

IntelliJ provides a default Maven that is bundled with the installer. Follow these instructions to install Apache Maven.

GPU setup

Follow the instructions below if you plan to use GPU setup.

1. Install CUDA and cuDNN Requirements:
   • CUDA 11.2
   • cuDNN 8.1.1

CUDA and cuDNN can be downloaded from here and here. Step by step installation guides can be found here.

2. Dependencies are needed to be included into Maven project if we wish to use GPU for training. Follow the links below for instructions in details.
   • ND4J backends for GPUs
   • Using Deeplearning4J with cuDNN

Parameters of Object detection

1. Anchor boxes: 8 anchor boxes
2. Classes: 12 classes (black king, black queen, black bishop, black knight, black rook, black pawn) and their white counterparts
3. updater: Adam's optimizer
4. transfer learning: Freeze the parameters (weights and biases) of the 5 first blocks in YOLOv2 achitecture
5. learning rate: 1e-4
6. Threshold: 0.6 (for detection), 0.5(NMS)
7. Number of epochs: 100
8. batch size: 2 (it is recommended to use a larger value if the hardware permits it (size of RAM AND GPU RAM))

Contributing

Pull requests are welcome for fixing exisiting issues and/or adding any improvements.