NNPACK was used to optimize Darknet without using a GPU. It is useful for embedded devices using ARM CPUs.
Log in to Raspberry Pi using SSH.
Install PeachPy and confu
sudo pip install --upgrade git+https://github.com/Maratyszcza/PeachPy
sudo pip install --upgrade git+https://github.com/Maratyszcza/confu
Install Ninja
git clone https://github.com/ninja-build/ninja.git
cd ninja
git checkout release
./configure.py --bootstrap
export NINJA_PATH=$PWD
Install clang
sudo apt-get install clang
Install NNPACK-darknet
git clone https://github.com/digitalbrain79/NNPACK-darknet.git
cd NNPACK-darknet
confu setup
python ./configure.py --backend auto
$NINJA_PATH/ninja
sudo cp -a lib/* /usr/lib/
sudo cp include/nnpack.h /usr/include/
sudo cp deps/pthreadpool/include/pthreadpool.h /usr/include/
Build darknet-nnpack
git clone https://github.com/digitalbrain79/darknet-nnpack.git
cd darknet-nnpack
make
The weight files can be downloaded from the YOLO homepage.
YOLOv2-tiny
./darknet detector test cfg/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights data/person.jpg
YOLOv3-tiny
./darknet detector test cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights data/person.jpg
| Device | Model | Build Options | Prediction Time (seconds) |
|---|---|---|---|
| Raspberry Pi 3 | YOLOv2-tiny | NNPACK=1,ARM_NEON=1 | 1.8 |
| Raspberry Pi 3 | YOLOv2-tiny | NNPACK=0,ARM_NEON=0 | 31 |
| Raspberry Pi 3 | YOLOv3-tiny | NNPACK=1,ARM_NEON=1 | 2.0 |
| Raspberry Pi 3 | YOLOv3-tiny | NNPACK=0,ARM_NEON=0 | 32 |
| Raspberry Pi 4 | YOLOv3-tiny | NNPACK=1,ARM_NEON=1 | 1.4 |
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