| 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% | - |
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
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