深度学习：YOLO v3（darknet）——训练自己的VOC数据_ubuntu voc yolo3训练

论文：YOLOv3: An Incremental Improvement 

论文地址：https://pjreddie.com/media/files/papers/YOLOv3.pdf

darknet代码：https://github.com/AlexeyAB/darknet#how-to-compile-on-linux

本文配置的方法也主要是参考  https://github.com/AlexeyAB/darknet#how-to-compile-on-linux 下面的介绍。

配置及训练主要是下面5个步骤，每个步骤里面有细分：

一、编译darknet

github上有关于linux和windows的darknet的配置方法，因为我是在Ubuntu下，所以本文只介绍linux下的配置

1.下载源码

终端输入：

https://github.com/AlexeyAB/darknet.git

gitclone代码下来或者自己手动下载从github上下下来，然后解压。

2.修改Makefile文件

其他文件都不用动，只需要修改Makefile文件

未修改前的Makefile文件如下：

GPU=0
CUDNN=0
OPENCV=0
DEBUG=0
OPENMP=0
LIBSO=0
 
ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \
      -gencode arch=compute_30,code=sm_30 \
      -gencode arch=compute_35,code=sm_35 \
      -gencode arch=compute_50,code=[sm_50,compute_50] \
      -gencode arch=compute_52,code=[sm_52,compute_52] \
      -gencode arch=compute_61,code=[sm_61,compute_61]
 
# This is what I use, uncomment if you know your arch and want to specify
# ARCH=  -gencode arch=compute_52,code=compute_52
 
VPATH=./src/
EXEC=darknet
OBJDIR=./obj/
 
ifeq ($(LIBSO), 1)
LIBNAMESO=darknet.so
APPNAMESO=uselib
endif
 
CC=gcc
CPP=g++
NVCC=nvcc 
OPTS=-Ofast
LDFLAGS= -lm -pthread 
COMMON= 
CFLAGS=-Wall -Wfatal-errors
 
ifeq ($(DEBUG), 1) 
OPTS=-O0 -g
endif
 
CFLAGS+=$(OPTS)
 
ifeq ($(OPENCV), 1) 
COMMON+= -DOPENCV
CFLAGS+= -DOPENCV
LDFLAGS+= `pkg-config --libs opencv` 
COMMON+= `pkg-config --cflags opencv` 
endif
 
ifeq ($(OPENMP), 1)
CFLAGS+= -fopenmp
LDFLAGS+= -lgomp
endif
 
ifeq ($(GPU), 1) 
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif
 
ifeq ($(CUDNN), 1) 
COMMON+= -DCUDNN 
CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn
endif
 
OBJ=gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o tree.o
ifeq ($(GPU), 1) 
LDFLAGS+= -lstdc++ 
OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
endif
 
OBJS = $(addprefix $(OBJDIR), $(OBJ))
DEPS = $(wildcard src/*.h) Makefile
 
all: obj backup results $(EXEC) $(LIBNAMESO) $(APPNAMESO)
 
ifeq ($(LIBSO), 1) 
CFLAGS+= -fPIC
 
$(LIBNAMESO): $(OBJS)
	$(CPP) -shared -std=c++11 -fvisibility=hidden -DYOLODLL_EXPORTS $(COMMON) $(CFLAGS) $^ -o $@ src/yolo_v2_class.cpp $(LDFLAGS)
	
$(APPNAMESO): $(OBJS)
	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)
endif
 
$(EXEC): $(OBJS)
	$(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)
 
$(OBJDIR)%.o: %.c $(DEPS)
	$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
 
$(OBJDIR)%.o: %.cu $(DEPS)
	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
 
obj:
	mkdir -p obj
backup:
	mkdir -p backup
results:
	mkdir -p results
 
.PHONY: clean
 
clean:
	rm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)

主要只是改前面几个即可，修改的参数介绍如下：

• GPU=1 to build with CUDA to accelerate by using GPU (CUDA should be in /usr/local/cuda)
• CUDNN=1 to