DeepLearning Suite is a set of tool that simplify the evaluation of most common object detection datasets with several object detection neural networks.
The idea is to offer a generic infrastructure to evaluates object detection algorithms againts a dataset and compute most common statistics:
- Intersecion Over Union
- Precision
- Recall
- YOLO
- Jderobot recorder logs
- Princeton RGB dataset [1]
- Spinello dataset [2]
- YOLO (darknet)
- Background substraction
Sample Generation Tool has been developed in order to simply the process of generation samples for datasets focused on object detection. The tools provides some features to reduce the time on labeling objects as rectangles.
# NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub && \
apt-key adv --export --no-emit-version -a $NVIDIA_GPGKEY_FPR | tail -n +5 > cudasign.pub && \
echo "$NVIDIA_GPGKEY_SUM cudasign.pub" | sha256sum -c --strict - && rm cudasign.pub && \
echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list
# apt-get install -y cuda
# apt-get install -y build-essential git cmake rapidjson-dev libboost-dev sudo
# apt-get install libopencv-dev
# apt-get install -y libboost-filesystem-dev libboost-system-dev libboost-thread-dev libeigen3-dev libgoogle-glog-dev \
libgsl-dev libgtkgl2.0-dev libgtkmm-2.4-dev libglademm-2.4-dev libgnomecanvas2-dev libgoocanvasmm-2.0-dev libgnomecanvasmm-2.6-dev \
libgtkglextmm-x11-1.2-dev libyaml-cpp-dev icestorm zeroc-ice libxml++2.6-dev qt5-default libqt5svg5-dev libtinyxml-dev \
catkin libssl-dev
git clone https://github.com/JdeRobot/ThirdParty
cd ThirdParty
cd qflightinstruments
qmake qfi.pro
make -j4
make install
git clone https://github.com/JdeRobot/JdeRobot
cd JdeRobot
cmake . -DENABLE_ROS=OFF
make -j4
cmake .
sudo make install
git clone https://github.com/JdeRobot/darknet && \
cd darknet && \
cmake . -DCMAKE_INSTALL_PREFIX=<DARKNET_DIR> && \
make -j4 && \
sudo make -j4 install && \
cmake . && \
rm -rf * && \
cmake . -DCMAKE_INSTALL_PREFIX=$DARKNET_DIR && \
make -j4 && \
sudo make -j4 install
Change <DARKNET_DIR> to your custom installation path.
Once you have all the deps installed just:
git clone https://github.com/JdeRobot/DeepLearningSuite
cd DeepLearningSuite
cd DeepLearningSuite/
cmake . -DDARKNET_PATH=<DARKNET_DIR>
[1] http://tracking.cs.princeton.edu/dataset.html
[2] http://www2.informatik.uni-freiburg.de/~spinello/RGBD-dataset.html
[3] YOLO: https://pjreddie.com/darknet/yolo/
[4] YOLO with c++ API: https://github.com/jderobot/darknet