The rest of this repository contains the code base used on the article(s) to be published.

You will need CUDA 10.1 in order to run mxnet 1.51, on Ubuntu:

sudo apt update
sudo apt install nvidia-cuda-toolkit

Add Nvidia repository:

sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list

Install CUDA 10.1

sudo apt-get update && sudo apt-get -o Dpkg::Options::="--force-overwrite" install cuda-10-1 cuda-drivers

Create a environment (I use conda):

conda create -n traindet python=3.7.6
conda activate traindet

Install packages:

git clone https://github.com/czrcbl/train_detection
cd train_detection
pip install -r requirements.txt

In order to run some scripts, you need to install the following packages:

git clone https://github.com/czrcbl/bboxes
cd bboxes
pip install -e .

git clone https://github.com/czrcbl/detection
cd detection
pip install -e .

In order to run the rendering code, you need the blender executable, version 2.80, in the path. Download from HERE.

• Folder traindet has the core utilities from training and evaluating the models.
• Folder rendering has all the code that is supposed to be run through blender. You must have the blender executable on system path.
• Folder scripts has the scripts for launching the networks training, starting the rendering of synthetic images and producing some visualizations.
• All the data necessary to train the models should be placed on the data folder.

Just call the model train scrip with the required arguments, models are saved under data/chackpoints/<dataset_name>/<model_name>: Some example calls are on the command.py file.