darknet/README.md

Model Zoo

Model	Test Size	APval	AP50val	AP75val	APSval	APMval	APLval	batch1 throughput
YOLOv4-CSP	640	49.1%	67.7%	53.8%	32.1%	54.4%	63.2%	76 fps
YOLOR-CSP	640	49.2%	67.6%	53.7%	32.9%	54.4%	63.0%	-
YOLOv4-CSP-X	640	50.9%	69.3%	55.4%	35.3%	55.8%	64.8%	53 fps
YOLOR-CSP-X	640	51.1%	69.6%	55.7%	35.7%	56.0%	65.2%	-

Installation

https://github.com/AlexeyAB/darknet

Docker environment (recommended)

Expand

# get code
git clone https://github.com/AlexeyAB/darknet

# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolor -it -v your_coco_path/:/coco/ -v your_code_path/:/yolor --shm-size=64g nvcr.io/nvidia/pytorch:21.02-py3

# apt install required packages
apt update
apt install -y libopencv-dev

# edit Makefile
#GPU=1
#CUDNN=1
#CUDNN_HALF=1
#OPENCV=1
#AVX=1
#OPENMP=1
#LIBSO=1
#ZED_CAMERA=0
#ZED_CAMERA_v2_8=0
#
#USE_CPP=0
#DEBUG=0
#
#ARCH= -gencode arch=compute_52,code=[sm_70,compute_70] \
#      -gencode arch=compute_61,code=[sm_75,compute_75] \
#      -gencode arch=compute_61,code=[sm_80,compute_80] \
#      -gencode arch=compute_61,code=[sm_86,compute_86]
#
#...

# build
make -j8

Testing

To reproduce inference speed, using:

CUDA_VISIBLE_DEVICES=0 ./darknet detector demo cfg/coco.data cfg/yolov4-csp.cfg weights/yolov4-csp.weights source/test.mp4 -dont_show -benchmark