yolo3源码分析

其他 2018-06-25 13:53:55 阅读次数: 2

要想更好地调参或者修改相应的功能就必须对源码有所了解，为此先看一下安装目录再往下讲解是很必要的。


顺便粘贴一下makefile

GPU=0
CUDNN=0
OPENCV=0
OPENMP=0
DEBUG=0

ARCH= -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_20,code=[sm_20,sm_21] \ This one is deprecated?

# 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/:./examples
SLIB=libdarknet.so
ALIB=libdarknet.a
EXEC=darknet
OBJDIR=./obj/

CC=gcc
NVCC=nvcc 
AR=ar
ARFLAGS=rcs
OPTS=-Ofast
LDFLAGS= -lm -pthread 
COMMON= -Iinclude/ -Isrc/
CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC

ifeq ($(OPENMP), 1) 
CFLAGS+= -fopenmp
endif

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 ($(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 deconvolutional_layer.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 detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o
EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o darknet.o
ifeq ($(GPU), 1) 
LDFLAGS+= -lstdc++ 
OBJ+=convolutional_kernels.o deconvolutional_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 avgpool_layer_kernels.o
endif

EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
OBJS = $(addprefix $(OBJDIR), $(OBJ))
DEPS = $(wildcard src/*.h) Makefile include/darknet.h

all: obj backup results $(SLIB) $(ALIB) $(EXEC)
#all: obj  results $(SLIB) $(ALIB) $(EXEC)


$(EXEC): $(EXECOBJ) $(ALIB)
	$(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)

$(ALIB): $(OBJS)
	$(AR) $(ARFLAGS) $@ $^

$(SLIB): $(OBJS)
	$(CC) $(CFLAGS) -shared $^ -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) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*


分析过程

首先我们从yolo的训练命令开始分析（yolo的源码是用c++写的，c占大部分）：
[html]  view plain  copy
  1. ./darknet detector train cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23   
从这里我们可以看出yolo主函数main中的参数argv[]在其中对应的值分别是 argv[0] -> darknet argv[1] -> detector argv[2] -> train .....（剩下的自己看），从这里我们可以看出，yolo主函数main一定在 examples/darknet.c 中,让我们来看一下主函数：

总结：根据参数选择执行函数，加载数据，配置，权重

int main(int argc, char **argv)
{
    //test_resize("data/bad.jpg");
    //test_box();
    //test_convolutional_layer();
    if(argc < 2){
        fprintf(stderr, "usage: %s <function>\n", argv[0]);
        return 0;
    }
    gpu_index = find_int_arg(argc, argv, "-i", 0);
    if(find_arg(argc, argv, "-nogpu")) {
        gpu_index = -1;
    }

#ifndef GPU
    gpu_index = -1;
#else
    if(gpu_index >= 0){
        cuda_set_device(gpu_index);
    }
#endif

    if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "yolo")){
        run_yolo(argc, argv);
    } else if (0 == strcmp(argv[1], "voxel")){
        run_voxel(argc, argv);
    } else if (0 == strcmp(argv[1], "super")){
        run_super(argc, argv);
    } else if (0 == strcmp(argv[1], "lsd")){
        run_lsd(argc, argv);
    } else if (0 == strcmp(argv[1], "detector")){
        run_detector(argc, argv);
    } else if (0 == strcmp(argv[1], "detect")){
        float thresh = find_float_arg(argc, argv, "-thresh", .24);
        char *filename = (argc > 4) ? argv[4]: 0;
        char *outfile = find_char_arg(argc, argv, "-out", 0);
        int fullscreen = find_arg(argc, argv, "-fullscreen");
        test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);
    } else if (0 == strcmp(argv[1], "cifar")){
        run_cifar(argc, argv);
    } else if (0 == strcmp(argv[1], "go")){
        run_go(argc, argv);
    } else if (0 == strcmp(argv[1], "rnn")){
        run_char_rnn(argc, argv);
    } else if (0 == strcmp(argv[1], "vid")){
        run_vid_rnn(argc, argv);
    } else if (0 == strcmp(argv[1], "coco")){
        run_coco(argc, argv);
    } else if (0 == strcmp(argv[1], "classify")){
        predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
    } else if (0 == strcmp(argv[1], "classifier")){
        run_classifier(argc, argv);
    } else if (0 == strcmp(argv[1], "regressor")){
        run_regressor(argc, argv);
    } else if (0 == strcmp(argv[1], "segmenter")){
        run_segmenter(argc, argv);
    } else if (0 == strcmp(argv[1], "art")){
        run_art(argc, argv);
    } else if (0 == strcmp(argv[1], "tag")){
        run_tag(argc, argv);
    } else if (0 == strcmp(argv[1], "compare")){
        run_compare(argc, argv);
    } else if (0 == strcmp(argv[1], "dice")){
        run_dice(argc, argv);
    } else if (0 == strcmp(argv[1], "writing")){
        run_writing(argc, argv);
    } else if (0 == strcmp(argv[1], "3d")){
        composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
    } else if (0 == strcmp(argv[1], "test")){
        test_resize(argv[2]);
    } else if (0 == strcmp(argv[1], "captcha")){
        run_captcha(argc, argv);
    } else if (0 == strcmp(argv[1], "nightmare")){
        run_nightmare(argc, argv);
    } else if (0 == strcmp(argv[1], "rgbgr")){
        rgbgr_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "reset")){
        reset_normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "denormalize")){
        denormalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "statistics")){
        statistics_net(argv[2], argv[3]);
    } else if (0 == strcmp(argv[1], "normalize")){
        normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "rescale")){
        rescale_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "ops")){
        operations(argv[2]);
    } else if (0 == strcmp(argv[1], "speed")){
        speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
    } else if (0 == strcmp(argv[1], "oneoff")){
        oneoff(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "oneoff2")){
        oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
    } else if (0 == strcmp(argv[1], "partial")){
        partial(argv[2], argv[3], argv[4], atoi(argv[5]));
    } else if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "visualize")){
        visualize(argv[2], (argc > 3) ? argv[3] : 0);
    } else if (0 == strcmp(argv[1], "mkimg")){
        mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);
    } else if (0 == strcmp(argv[1], "imtest")){
        test_resize(argv[2]);
    } else {
        fprintf(stderr, "Not an option: %s\n", argv[1]);
    }
    return 0;
}

这里先不展开，可以参考下面的文章，讲的很详细了。

https://blog.csdn.net/u014540717/article/details/53114067

https://blog.csdn.net/syoung9029/article/details/70338061

理解到这里基本下就可以对源码（darknet.c）修改实现自己的功能了。

                                YOLOv3批量测试图片并保存在自定义文件夹下

转子：https://blog.csdn.net/mieleizhi0522/article/details/79989754

 先说测试并返回评价指标的3个命令

1) ./darknet detector test cfg/voc.data cfg/yolo-voc.cfg ./svt/backup/yolo-voc_final.weights

/*不现实评价指标，输入图片路径，只显示框好后的图片和类别、置信率*/

 

2) ./darknet detector valid cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_final.weights

/*在终端只返回用时，在./results/comp4_det_test_[类名].txt里保存测试结果*/

 

3) ./darknet detector recall cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_final.weights（这个命令需修改dectector.c文件）

/*依次ID:图片序号从0开始，correct:累计检测正确的总框数，total:累计的总ground truth数，RPs/Img: 累计的总proposals/已检测图片数，IOU，Recall: correct / total，proposals:累计的总框数，Precision: correct / proposals*/

*************************************************************************************************

命令参数总结

  1. 训练模型
    • 单GPU训练：./darknet -i <gpu_id> detector train <data_cfg> <train_cfg> <weights>  举例：
      ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74
    • 多GPU训练，格式为0,1,2,3：./darknet detector train <data_cfg> <model_cfg> <weights> -gpus <gpu_list> 举例          
      ./darknet detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74 -gpus 0,1,2,3
    • CPU训练：./darknet -nogpu detector train <data_cfg> <model_cfg> <weights>
  2. 测试图片
    • 测试单张图片，需要编译时有OpenCV支持：./darknet detector test <data_cfg> <test_cfg> <weights> <image_file>
    • <test_cfg>文件中batch和subdivisions两项必须为1。
    • 测试时还可以用-thresh和-hier选项指定对应参数。
  3. 生成预测结果
    • ./darknet detector valid <data_cfg> <test_cfg> <weights> <out_file>
    • <test_cfg>文件中batch和subdivisions两项必须为1。
    • 结果生成在<data_cfg>的results指定的目录下以<out_file>开头的若干文件中，若<data_cfg>没有指定results，那么默认为<darknet_root>/results。
  4. 计算recall（执行这个命令需要修改detector.c文件，修改信息请参考“detector.c修改”）
    • ./darknet detector recall <data_cfg> <test_cfg> <weights>
    • <test_cfg>文件中batch和subdivisions两项必须为1。
    • 输出在stderr里，重定向时请注意。
    • RPs/Img、IOU、Recall都是到当前测试图片的均值。
    • detector.c中对目录处理有错误，可以参照validate_detector对validate_detector_recall最开始几行的处理进行修改。
  5. 执行这些命令的时候在<darknet-root>下进行。

**************************************************************************************************************

detector.c修改（example文件夹下）


 validate_detector_recall函数定义和调用改为：

[cpp] view plain copy print ?
  1. void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)  
  2. validate_detector_recall(datacfg, cfg, weights);  
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
validate_detector_recall(datacfg, cfg, weights);

validate_detector_recall内的plist和paths的如下初始化代码：

[cpp] view plain copy print ?
  1. list *plist = get_paths("data/voc.2007.test");  
  2. char **paths = (char **)list_to_array(plist);  
list *plist = get_paths("data/voc.2007.test");
char **paths = (char **)list_to_array(plist);

修改为：

[cpp] view plain copy print ?
  1. list *options = read_data_cfg(datacfg);  
  2. char *valid_images = option_find_str(options, "valid", "data/train.list");  
  3. list *plist = get_paths(valid_images);  
  4. char **paths = (char **)list_to_array(plist);  
list *options = read_data_cfg(datacfg);
char *valid_images = option_find_str(options, "valid", "data/train.list");
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);


上述修改完之后务必记住要在darknet下重新make一下就可以进行recall命令了，

[objc] view plain copy print ?
  1. ./darknet detector recall cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_final.weights  
*************************************** 正文如下*********************************************************

批量测试图片并保存在自定义文件夹下

 1.用下面代码替换detector.c文件（example文件夹下）的void test_detector函数（注意有3处要改成自己的路径）


void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    char **names = get_labels(name_list);

    image **alphabet = load_alphabet();
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    srand(2222222);
    double time;
    char buff[256];
    char *input = buff;
    float nms=.45;
    int i=0;
    while(1){
        if(filename){
            strncpy(input, filename, 256);
            image im = load_image_color(input,0,0);
            image sized = letterbox_image(im, net->w, net->h);
        //image sized = resize_image(im, net->w, net->h);
        //image sized2 = resize_max(im, net->w);
        //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
        //resize_network(net, sized.w, sized.h);
            layer l = net->layers[net->n-1];


            float *X = sized.data;
            time=what_time_is_it_now();
            network_predict(net, X);
            printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
            int nboxes = 0;
            detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
            //printf("%d\n", nboxes);
            //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
            if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
                draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
                free_detections(dets, nboxes);
            if(outfile)
             {
                save_image(im, outfile);
             }
            else{
                save_image(im, "predictions");
#ifdef OPENCV
                cvNamedWindow("predictions", CV_WINDOW_NORMAL); 
                if(fullscreen){
                cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
                }
                show_image(im, "predictions");
                cvWaitKey(0);
                cvDestroyAllWindows();
#endif
            }
            free_image(im);
            free_image(sized);
            if (filename) break;
         } 
        else {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
   
            list *plist = get_paths(input);
            char **paths = (char **)list_to_array(plist);
             printf("Start Testing!\n");
            int m = plist->size;
            if(access("/home/FENGsl/darknet/data/out",0)==-1)//"/home/FENGsl/darknet/data"修改成自己的路径
            {
              if (mkdir("/home/FENGsl/darknet/data/out",0777))//"/home/FENGsl/darknet/data"修改成自己的路径
               {
                 printf("creat file bag failed!!!");
               }
            }
            for(i = 0; i < m; ++i){
             char *path = paths[i];
             image im = load_image_color(path,0,0);
             image sized = letterbox_image(im, net->w, net->h);
        //image sized = resize_image(im, net->w, net->h);
        //image sized2 = resize_max(im, net->w);
        //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
        //resize_network(net, sized.w, sized.h);
        layer l = net->layers[net->n-1];


        float *X = sized.data;
        time=what_time_is_it_now();
        network_predict(net, X);
        printf("Try Very Hard:");
        printf("%s: Predicted in %f seconds.\n", path, what_time_is_it_now()-time);
        int nboxes = 0;
        detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
        //printf("%d\n", nboxes);
        //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
        draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
        free_detections(dets, nboxes);
        if(outfile){
            save_image(im, outfile);
        }
        else{
             
             char b[2048];
            sprintf(b,"/home/FENGsl/darknet/data/out/%s",GetFilename(path));//"/home/FENGsl/darknet/data"修改成自己的路径
            
            save_image(im, b);
            printf("save %s successfully!\n",GetFilename(path));
#ifdef OPENCV
            cvNamedWindow("predictions", CV_WINDOW_NORMAL); 
            if(fullscreen){
                cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
            }
            show_image(im, "predictions");
            cvWaitKey(0);
            cvDestroyAllWindows();
#endif
        }

        free_image(im);
        free_image(sized);
        if (filename) break;
        }
      }
    }
}

2，在函数前面添加*GetFilename(char *p)函数

#include "darknet.h"
#include <sys/stat.h>
#include<stdio.h>
#include<time.h>
#include<sys/types.h>
static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};

char *GetFilename(char *p)
{ 
    char name[20]={""};
    char *q = strrchr(p,'/') + 1;
    strncpy(name,q,6);
    return name;
}

3.在darknet下重新make

4.执行批量测试命令如下

./darknet detector test cfg/voc.data cfg/yolov3-voc.cfg backup/yolov3-voc_final.weights
layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32  0.299 BFLOPs
    1 conv     64  3 x 3 / 2   416 x 416 x  32   ->   208 x 208 x  64  1.595 BFLOPs
    .......
  104 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256  1.595 BFLOPs
  105 conv    255  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 255  0.353 BFLOPs
  106 detection
Loading weights from yolov3.weights...Done!
Enter Image Path:

Enter Image Path：后面输入你的txt文件路径（你准备好的所有测试图片的路径全部存放在一个txt文件里），你可以复制voc.data文件里的valid后面的路径，就可以了，如下

[objc] view plain copy print ?
  1. classes= 3  
  2. train  =/home/FENGsl/darknet/data/train.txt  
  3. valid  = /home/FENGsl/darknet/data/2007_test.txt  
  4. names = data/voc.names  
  5. backup = backup  
classes= 3
train  =/home/FENGsl/darknet/data/train.txt
valid  = /home/FENGsl/darknet/data/2007_test.txt
names = data/voc.names
backup = backup

你就可以看到如下结果：

[objc] view plain copy print ?
  1.   101 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128  0.177 BFLOPs  
  2.   102 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256  1.595 BFLOPs  
  3.   103 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128  0.177 BFLOPs  
  4.   104 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256  1.595 BFLOPs  
  5.   105 conv     24  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x  24  0.033 BFLOPs  
  6.   106 detection  
  7. Loading weights from backup/yolov3-voc_final.weights...Done!  
  8. Enter Image Path: /home/FENGsl/darknet/data/2007_test.txt  
  9. Start Testing!  
  10. Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000013.jpg: Predicted in 0.085814 seconds.  
  11. PED: 100%  
  12. save 000013 successfully!  
  13. Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000016.jpg: Predicted in 0.084692 seconds.  
  14. save 000016 successfully!                                                                                        
  101 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128  0.177 BFLOPs
  102 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256  1.595 BFLOPs
  103 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128  0.177 BFLOPs
  104 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256  1.595 BFLOPs
  105 conv     24  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x  24  0.033 BFLOPs
  106 detection
Loading weights from backup/yolov3-voc_final.weights...Done!
Enter Image Path: /home/FENGsl/darknet/data/2007_test.txt
Start Testing!
Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000013.jpg: Predicted in 0.085814 seconds.
PED: 100%
save 000013 successfully!
Try Very Hard:/home/FENGsl/darknet/data/VOCdevkit/VOC2007/JPEGImages/000016.jpg: Predicted in 0.084692 seconds.
save 000016 successfully!

然后你所有的图片都保存在了data/out文件夹下，你可以打开看看，展示一下我的结果




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