文章目录[隐藏]
- 1、进行模型训练数据准备
- 2、修改配置文件
- 2.1 新建cfg/vechle.names
- 2.3 根据所选模型的不同，设置不同的配置文件
- 2.3.1 新建cfg/yolov4_vechle.cfg
- 2.3.2 新建cfg/yolov4_tiny_vechle.cfg
- 2.3.3 新建cfg/yolo3_vechcle.cfg
- 2.3.4 新建cfg/yolo3_vechcle_mosaic.cfg
- 2.3.5 新建cfg/yolo3_tiny_vechcle.cfg
- 2.3.6 新建cfg/enetb0_vechcle.cfg
- 2.3.7 新建cfg/yolov4_p6_vechcle.cfg
- 2.3.8 新建cfg/yolov4_p5_vechcle.cfg
- 2.3.9 新建cfg/yolov4_csp_x_swish_vechcle.cfg
- 2.3.10 新建cfg/yolov4_csp_swish_vechcle.cfg
- 2.3.11 新建cfg/yolov4x_mish_vechcle.cfg
- 2.3.12 新建cfg/yolov4_csp_vechcle.cfg
- 3.训练自己的数据集
- 4、模型的推理
- 5、模型性能统计测试
- 6、Anchor Box先验框聚类分析与修改
- 总结
本文是继上一篇
数据准备之后，进行模型训练的全流程。本文直接从jupter notebook转换而来，都经过验证，但格式可能会有点问题。
1、进行模型训练数据准备
使用的数据集是VOC2007_DEST，VOC2012_DEST，COCO_VOC_DEST,分类是bicycle,bus,car,motorbike,truck五个分类,将使用darknet，这些个分类的来源，请查看本节其它的jupyter
1.1 划分训练和验证集
import os
def create_train_val(datasets=[("VOC2007_DEST",1.0),("VOC2012_DEST",1.0),("COCO_VOC_DEST",0.95)]):
wd = os.getcwd()
for dataset,percent in datasets:
img_files = os.listdir('%s/VOCdevkit/%s/JPEGImages' %(wd,dataset))
split = int(len(img_files) * percent)
train_img_files,val_img_files = img_files[:split],img_files[split:]
with open('%s/VOCdevkit/%s/ImageSets/Main/train.txt' %(wd,dataset),'w') as f1:
for img_file in train_img_files:
f1.write(img_file.split('.')[0]+'\n')
print('train.txt done')
with open('%s/VOCdevkit/%s/ImageSets/Main/val.txt' %(wd,dataset),'w') as f2:
for img_file in val_img_files:
f2.write(img_file.split('.')[0]+'\n')
print('val.txt done')
create_train_val()
本代码支持多个VOC格式数据集的合并。数据的划分没有打散等操作，也可以使用skleran.model_select.train_test_split来划分。
如下：
mport os
from sklearn.model_selection import train_test_split
def create_train_val(datasets=[("VOC2007_DEST",1.0),("VOC2012_DEST",1.0),("COCO_VOC_DEST",0.95)]):
wd = os.getcwd()
for dataset,percent in datasets:
img_files = os.listdir('%s/VOCdevkit/%s/JPEGImages' %(wd,dataset))
if percent<1.0:
train_img_files,val_img_files = train_test_split(img_files,train_size=percent)
else:
train_img_files,val_img_files=img_files,[]
with open('%s/VOCdevkit/%s/ImageSets/Main/train.txt' %(wd,dataset),'w') as f1:
for img_file in train_img_files:
f1.write(img_file.split('.')[0]+'\n')
print('train.txt done')
with open('%s/VOCdevkit/%s/ImageSets/Main/val.txt' %(wd,dataset),'w') as f2:
for img_file in val_img_files:
f2.write(img_file.split('.')[0]+'\n')
print('val.txt done')
1.2 将数据标注格式转换为YOLO格式
import os
import shutil
from tqdm import tqdm
import xml.etree.ElementTree as ET
def convert(size, box):
dw = 1./size[0]
dh = 1./size[1]
x = (box[0] + box[1])/2.0
y = (box[2] + box[3])/2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_yolo(datasets=[('VOC2007_DEST','train'),('VOC2007_DEST','val')],classes=['bicycle','bus','car','motorbike','truck']):
wd = os.getcwd()
#直接把原有结果全部清理
for i,(dataset, image_set) in enumerate(datasets):
dest_path = 'VOCdevkit/%s/labels/'%(dataset)
if os.path.exists(dest_path):
shutil.rmtree(dest_path)
#重新生成新的数据集
for i,(dataset, image_set) in enumerate(datasets):
dest_path = 'VOCdevkit/%s/labels/'%(dataset)
if not os.path.exists(dest_path):
os.makedirs(dest_path)
for i,(dataset, image_set) in tqdm(enumerate(datasets)):
image_ids = open('VOCdevkit/%s/ImageSets/Main/%s.txt'%(dataset, image_set)).read().strip().split()
list_file = open('%s_%s.txt'%(dataset, image_set), 'w')
for image_id in image_ids:
list_file.write('%s/VOCdevkit/%s/JPEGImages/%s.jpg\n'%(wd, dataset, image_id))
try: #对于有标记文件的数据可以进行转换
in_file = open('VOCdevkit/%s/Annotations/%s.xml'%(dataset, image_id))
out_file = open('VOCdevkit/%s/labels/%s.txt'%(dataset, image_id), 'w')
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
# print image_id
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h),b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
out_file.close()
except:#对没有标记的图片，称为背景图片，则产生的yolo标注文件是空的即可
out_file = open('VOCdevkit/%s/labels/%s.txt'%(dataset, image_id), 'w')
out_file.close()
list_file.close()
train_txt=[a+'_'+b+'.txt' for a,b in datasets if b=='train']
if len(train_txt):
strs_train = 'cat '+ ' '.join(train_txt) +' > cfg/train.txt'
os.system(strs_train)
val_txt = [a+'_'+b+'.txt' for a,b in datasets if b=='val']
if len(val_txt):
strs_val = 'cat '+ ' '.join(val_txt) +'> cfg/val.txt'
os.system(strs_val)
train_val_txt = [a+'_'+b+'.txt' for a,b in datasets]
if len(train_val_txt):
rm_txt = 'rm '+ ' '.join(train_val_txt)
os.system(rm_txt)
print("all Done!")
convert_yolo(datasets=[('VOC2007_DEST','train'),('VOC2007_DEST','val'),('VOC2012_DEST','train'),('VOC2012_DEST','val'),('COCO_VOC_DEST','train'),('COCO_VOC_DEST','val')],classes=['bicycle','bus','car','motorbike','truck'])
# convert_yolo()
6it [00:04, 1.24it/s]
all Done!
! ls VOCdevkit/VOC2012_DEST/labels
以上转换相对于原来darknet的转换脚本做了一点小改动，可以对有标注文件但没有标注框的标注文件进行转换。
2、修改配置文件
本次配置将使用多种模型，具体看下文：
2.1 新建cfg/vechle.names
vechle.names中存放我们要进行的分类，注意顺序
names = ['bicycle','bus','car','motorbike','truck']
with open('cfg/vechcle.names','w') as f:
for i,name in enumerate(names):
f.write(name)
if i != len(names)-1:
f.write('\n')
with open('cfg/vechcle.names') as f:
print(f.read())
bicycle
bus
car
motorbike
truck
2.2 新建cfg/vechle.data
可以复制datknet cfg/voc.data再根据自己的情况进行修改
datas=["classes= 5",
"train = /workspace/yolo_demo/cfg/train.txt",
"valid = /workspace/yolo_demo/cfg/val.txt",
"names = /workspace/yolo_demo/cfg/vechcle.names",
"backup = /workspace/yolo_demo/backup"
]
with open('cfg/vechcle.data','w') as f:
for i,data in enumerate(datas):
f.write(data)
if i != len(datas)-1:
f.write('\n')
with open('cfg/vechcle.data') as f:
print(f.read())
classes= 5
train = /workspace/yolo_demo/cfg/train.txt
valid = /workspace/yolo_demo/cfg/val.txt
names = /workspace/yolo_demo/cfg/vechcle.names
backup = /workspace/yolo_demo/backup
2.3 根据所选模型的不同，设置不同的配置文件
上图为不同模型的对比结果。下面将列出不同模型的在MSCOCO数据集上的预训练模型及相关说明，FPS是在 RTX 2070 ® 或 Tesla V100 (V)上的结果
| 配置文件 | 输入大小 | mAP@0.5 | mAP@0.5:0.95 | FPS | 大小 | 预训练模型 |
|---|---|---|---|---|---|---|
| yolov4-p6.cfg | 1280x1280 | 72.1% | 54.0% | 32(V) | 487MB | yolov4-p6.conv.289 |
| yolov4-p5.cfg | 896x896 | 10.0% | 51.6% | 43(V) | 271MB | yolov4-p5.conv.232 |
| yolov4-csp-x-swish.cfg | 640x640 | 69.9% | 51.5% | 23®50(V) | 381MB | yolov4-csp-x-swish.conv.192 |
| yolov4-csp-swish.cfg | 640x640 | 68.7% | 50% | 70(V) | 202M | yolov4-csp-swish.conv.164 |
| yolov4x-mish.cfg | 640x640 | 68.5% | 50.1% | 23®50(V) | 381M | yolov4x-mish.conv.166 |
| yolov4-csp.cfg | 640x640 | 67.4% | 48.7% | 70(V) | 202M | yolov4-csp.conv.142 |
| yolov4-csp.cfg | 512x512 | 64.8% | 46.2% | 93(V) | 202M | yolov4-csp.conv.142 |
| yolov4.cfg | 608x608 | 65.7% | 43.5% | 34®64(V) | 245M | yolov4.conv.137 |
| yolov4.cfg | 512x512 | 64.9% | 43.0% | 45®83(V) | 245M | yolov4.conv.137 |
| yolov4.cfg | 416x416 | 62.8% | 41.2% | 55®96(V) | 245M | yolov4.conv.137 |
| yolov4.cfg | 320x320 | 60% | 38% | 63®123(V) | 245M | yolov4.conv.137 |
| yolov4-tiny.cfg | 416x416 | 40% | -% | 330®371(1080Ti) | 23.1M | yolov4-tiny.conv.29 |
| yolov3.cfg | 414x416 | 55.3% | -% | 66® | 236M | darknet53.conv.74 |
| yolov3-tiny.cfg | 416x416 | 33.1% | -% | 370® | 33.7M | yolov3-tiny.conv.11 |
| enet-coco.cfg(yolov3 efficientnetb0) | None | 45.5% | -% | 55® | 18.3M | enetb0-coco.conv.74 |
对于上述众多模型，会选择其中部分进行训练并获得训练好的模型,所有工作将在两台有8个V100的机器上进行
所有配置中的6,7行来控制显存的使用
2.3.1 新建cfg/yolov4_vechle.cfg
可以复制darnet下cfg/yolov4-custom.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为416，608，832等其它32的倍数,这里设置608
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第744行：stopbackward的数值表示迭代这么多次后，前边的层将停止更新，可以注释掉，用来做迁移学习
- 第970，1058，1146行：修改classes为我们的具体分类数
- 第963，1051，1139行：修改filters为（classes+5)*3
2.3.2 新建cfg/yolov4_tiny_vechle.cfg
可以复制darnet下cfg/yolov4-tiny-custom.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为416，608，832等其它32的倍数，这里设置608
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第220,269行：修改classes为我们的具体分类数
- 第212,263行：修改filters为（classes+5)*3
2.3.3 新建cfg/yolo3_vechcle.cfg
可以复制darnet下cfg/yolov3-voc.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为416，608，832等其它32的倍数，这里设置608
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第611,695,779行：修改classes为我们的具体分类数
- 第605,689,773行：修改filters为（classes+5)*3
2.3.4 新建cfg/yolo3_vechcle_mosaic.cfg
可以复制darnet下cfg/yolov3-voc.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为416，608，832等其它32的倍数，这里设置608
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第25行：加mosaic=1
- 第612,696,780行：修改classes为我们的具体分类数
- 第606,690,774行：修改filters为（classes+5)*3
2.3.5 新建cfg/yolo3_tiny_vechcle.cfg
可以复制darnet下cfg/yolov3-tiny.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为416，608，832等其它32的倍数，这里设置608
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第135,177行：修改classes为我们的具体分类数
- 第127,171行：修改filters为（classes+5)*3
2.3.6 新建cfg/enetb0_vechcle.cfg
模型大小于yolov4 的十分之一，精度与416x416相当，速度也相当,所以用efficient b0做backbone
可以复制darnet下cfg/enet-coco.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为416，608，832等其它32的倍数
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第1015,1066行：修改classes为我们的具体分类数
- 第1007,1060行：修改filters为（classes+5)*3
2.3.7 新建cfg/yolov4_p6_vechcle.cfg
可以复制darnet下cfg/yolov4_p6.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为1280
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第2144,2189,2234,2279行：修改classes为我们的具体分类数
- 第2136,2189,2226,2271行：修改filters为（classes+5)*4
2.3.8 新建cfg/yolov4_p5_vechcle.cfg
可以复制darnet下cfg/yolov4_p5.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为896
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第1728,1773,1818行：修改classes为我们的具体分类数
- 第1720,1765,1810行：修改filters为（classes+5)*4
2.3.9 新建cfg/yolov4_csp_x_swish_vechcle.cfg
可以复制darnet下cfg/yolov4-csp-x-swish.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为640
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第1453,1496,1539行：修改classes为我们的具体分类数
- 第1447,1490,1533行：修改filters为（classes+5)*3
2.3.10 新建cfg/yolov4_csp_swish_vechcle.cfg
可以复制darnet下cfg/yolov4-csp-swish.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为640
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第1252,1295,1338行：修改classes为我们的具体分类数
- 第1446,1489,1332行：修改filters为（classes+5)*3
2.3.11 新建cfg/yolov4x_mish_vechcle.cfg
可以复制darnet下cfg/yolov4x-mish.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为640
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第1159,1289,1419行：修改classes为我们的具体分类数
- 第1152,1282,1412行：修改filters为（classes+5)*3
2.3.12 新建cfg/yolov4_csp_vechcle.cfg
可以复制darnet下cfg/yolov4-csp.cfg,再根据自己情况进行修改,一次修改，以后基本不会变：
- 第8,9行：width,height 可以设置为640
- 第20行：max_batches 可以设置为分类数乘2000，但不少于图片数，所以建议设大一点，图片数乘5，第张图可以见到5次
- 第22行：steps分别是max_batches的0.8和0.9
- 第1034,1148,1262行：修改classes为我们的具体分类数
- 第1027,1141,1255行：修改filters为（classes+5)*3
3.训练自己的数据集
3.1 下载预训练权重
- yolov4 已下载好yolov4.conv.137，放到cfg目录下
- yolov4-tiny 已下载好yolov4-tiny.conv.29，放到cfg目录下
- yolov3 已下载好darknet53.conv.74，放到cfg目录下
- yolov3-tiny 已下载好yolov3-tiny.conv.11，放到cfg目录下
其它预训练权重也下载好了，参见2.3节中那个表中预训练模型的名称这里就不再一一列举了
3.2 开始模型的训练
训练的命令有,以下是多GPU训练：
CUDA_VISIBLE_DEVICES=0,1,2,3 nohup darknet detector train cfg/vechle.data cfg/vechle.cfg cfg/yolov4.conv.137 -gpus 0,1,2,3 -dont_show -map 2>&1 >logs/train20210713.log &
3.2.1 Yolo V4训练
with open('train_yolov4.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4_vechcle.cfg cfg/yolov4.conv.137 -gpus 6,7 -dont_show -map 2>&1 >logs/yolov4.log &")
with open('train_yolov4.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4_vechcle.cfg cfg/yolov4.conv.137 -gpus 6,7 -dont_show -map 2>&1 >logs/yolov4.log &
#建议在shell 中执行该命令
import os
os.system("sh train_yolov4.sh")
0
3.2.2 Yolo V4 tiny训练
with open('train_yolov4_tiny.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4_tiny_vechcle.cfg cfg/yolov4-tiny.conv.29 -gpus 5 -dont_show -map 2>&1 >logs/yolov4_tiny.log &")
with open('train_yolov4_tiny.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4_tiny_vechcle.cfg cfg/yolov4-tiny.conv.29 -gpus 5 -dont_show -map 2>&1 >logs/yolov4_tiny.log &
#建议在shell 中执行该命令
import os
os.system("sh train_yolov4_tiny.sh")
3.2.3 Yolo V3 训练
with open('train_yolov3.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov3_vechcle.cfg cfg/darknet53.conv.74 -gpus 3,4 -dont_show -map 2>&1 >logs/yolov3.log &")
with open('train_yolov3.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov3_vechcle.cfg cfg/darknet53.conv.74 -gpus 3,4 -dont_show -map 2>&1 >logs/yolov3.log &
3.2.4 Yolov3 加mosaic数据增强训练
with open('train_yolov3_mosaic.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov3_vechcle_mosaic.cfg cfg/darknet53.conv.74 -gpus 2,5 -dont_show -map 2>&1 >logs/yolov3_mosaic.log &")
with open('train_yolov3_mosaic.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov3_vechcle_mosaic.cfg cfg/darknet53.conv.74 -gpus 2,5 -dont_show -map 2>&1 >logs/yolov3_mosaic.log &
3.2.5 Yolo V3 tiny训练
with open('train_yolov3_tiny.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov3_tiny_vechcle.cfg cfg/yolov3-tiny.conv.11 -gpus 2 -dont_show -map 2>&1 >logs/yolov3_tiny.log &")
with open('train_yolov3_tiny.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov3_tiny_vechcle.cfg cfg/yolov3-tiny.conv.11 -gpus 2 -dont_show -map 2>&1 >logs/yolov3_tiny.log &
3.2.6 Yolo V3 efficientb0训练
with open('train_yolov3_enet.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/enetb0_vechcle.cfg cfg/enetb0-coco.conv.132 -gpus 1 -dont_show -map 2>&1 >logs/yolov3_enet.log &")
with open('train_yolov3_enet.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/enetb0_vechcle.cfg cfg/enetb0-coco.conv.132 -gpus 1 -dont_show -map 2>&1 >logs/yolov3_enet.log &
3.2.7 Yolo V4 p6训练
with open('train_yolov4_p6.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4_p6_vechcle.cfg cfg/yolov4-p6.conv.289 -gpus 6,7 -dont_show -map 2>&1 >logs/yolov4_p6.log &")
with open('train_yolov4_p6.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4_p6_vechcle.cfg cfg/yolov4-p6.conv.289 -gpus 6,7 -dont_show -map 2>&1 >logs/yolov4_p6.log &
3.2.8 Yolo V4 p5训练
with open('train_yolov4_p5.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4_p5_vechcle.cfg cfg/yolov4-p5.conv.232 -gpus 3,4 -dont_show -map 2>&1 >logs/yolov4_p5.log &")
with open('train_yolov4_p5.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4_p5_vechcle.cfg cfg/yolov4-p5.conv.232 -gpus 3,4 -dont_show -map 2>&1 >logs/yolov4_p5.log &
3.2.9 Yolo V4 csp x swish训练
with open('train_yolov4_csp_x_swish.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4_csp_x_swish_vechcle.cfg cfg/yolov4-csp-x-swish.conv.192 -gpus 0,1 -dont_show -map 2>&1 >logs/yolov4_csp_x_swish.log &")
with open('train_yolov4_csp_x_swish.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4_csp_x_swish_vechcle.cfg cfg/yolov4-csp-x-swish.conv.192 -gpus 0,1 -dont_show -map 2>&1 >logs/yolov4_csp_x_swish.log &
3.2.10 Yolo V4 csp swish训练
with open('train_yolov4_csp_swish.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4_csp_swish_vechcle.cfg cfg/yolov4-csp-swish.conv.164 -gpus 0,1 -dont_show -map 2>&1 >logs/yolov4_csp_swish.log &")
with open('train_yolov4_csp_swish.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4_csp_swish_vechcle.cfg cfg/yolov4-csp-swish.conv.164 -gpus 0,1 -dont_show -map 2>&1 >logs/yolov4_csp_swish.log &
3.2.11 Yolo V4 x mish训练
with open('train_yolov4x_mish.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4x_mish_vechcle.cfg cfg/yolov4x-mish.conv.166 -gpus 2,3 -dont_show -map 2>&1 >logs/yolov4x_mish.log &")
with open('train_yolov4x_mish.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4x_mish_vechcle.cfg cfg/yolov4x-mish.conv.166 -gpus 2,3 -dont_show -map 2>&1 >logs/yolov4x_mish.log &
3.2.12 Yolo V4 csp 训练
with open('train_yolov4_csp.sh','w') as f:
f.write("nohup darknet detector train cfg/vechcle.data cfg/yolov4_csp_vechcle.cfg cfg/yolov4-csp.conv.142 -gpus 6,7 -dont_show -map 2>&1 >logs/yolov4_csp.log &")
with open('train_yolov4x_mish.sh') as f:
print(f.read())
nohup darknet detector train cfg/vechcle.data cfg/yolov4x_mish_vechcle.cfg cfg/yolov4x-mish.conv.166 -gpus 2,3 -dont_show -map 2>&1 >logs/yolov4x_mish.log &
4、模型的推理
在模型使用时，修改配置文件batch=1 subdivisions=1用于推理。别外，可以能过修改.cfg文件中的height,width来修改模型输入。可以是608608，832832或其它什么可32的倍数。改大输入会对小目标有提升。这时模型无须再次训练，还用原来模型即可。当然要从概本上提升精度，还是要用大输入尺寸来训练模型。
4.1 命令行对图片和视频模型的推理
%ºsh
darknet detector test cfg/vechle.data cfg/vechle.cfg backup/vechle_best.weights /workspace/vechcle/VOCdevkit/VOC2007_DEST/JPEGImages/000004.jpg -thresh 0.25
net.optimized_memory = 0
mini_batch = 1, batch = 16, time_steps = 1, train = 0
Create CUDA-stream - 0
Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
seen 64, trained: 918 K-images (14 Kilo-batches_64)
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
/workspace/vechcle/VOCdevkit/VOC2007_DEST/JPEGImages/000004.jpg: Predicted in 19.198000 milli-seconds.
car: 89%
car: 77%
truck: 49%
truck: 28%
car: 33%
car: 97%
car: 98%
car: 96%
car: 98%
car: 99%
car: 99%
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
0 : compute_capability = 700, cudnn_half = 0, GPU: Tesla V100-SXM2-32GB
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
22 route 21 12 -> 152 x 152 x 128
23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF
24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF
25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
26 route 24 -> 76 x 76 x 256
27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
53 route 52 25 -> 76 x 76 x 256
54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF
55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF
56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
57 route 55 -> 38 x 38 x 512
58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
84 route 83 56 -> 38 x 38 x 512
85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF
86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF
87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
88 route 86 -> 19 x 19 x1024
89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
103 route 102 87 -> 19 x 19 x1024
104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF
105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF
109 route 107 -> 19 x 19 x 512
110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF
111 route 107 -> 19 x 19 x 512
112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF
113 route 112 110 108 107 -> 19 x 19 x2048
114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF
115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF
118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
119 route 85 -> 38 x 38 x 512
120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
121 route 120 118 -> 38 x 38 x 512
122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF
128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
129 route 54 -> 76 x 76 x 256
130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
131 route 130 128 -> 76 x 76 x 256
132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
138 conv 30 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 30 0.089 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
140 route 136 -> 76 x 76 x 128
141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF
142 route 141 126 -> 38 x 38 x 512
143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
149 conv 30 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 30 0.044 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
151 route 147 -> 38 x 38 x 256
152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF
153 route 152 116 -> 19 x 19 x1024
154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
160 conv 30 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 30 0.022 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 127.294
avg_outputs = 1047336
Allocate additional workspace_size = 52.43 MB
Loading weights from backup/vechle_best.weights...Done! Loaded 162 layers from weights-file
Unable to init server: Could not connect: 拒绝连接
OpenCV exception: show_image_cv
执行本代码后本身是会显示结果图的，但是由于本工作在docker中完成，并没有实现显示,结果保存在predictions.jpg
%ºsh
#指定GPU 6
darknet detector test cfg/vechle.data cfg/vechle.cfg backup/vechle_best.weights -i 6 /workspace/vechcle/VOCdevkit/VOC2007_DEST/JPEGImages/000004.jpg -thresh 0.25
net.optimized_memory = 0
mini_batch = 1, batch = 16, time_steps = 1, train = 0
Create CUDA-stream - 6
Create cudnn-handle 6
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
seen 64, trained: 918 K-images (14 Kilo-batches_64)
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
/workspace/vechcle/VOCdevkit/VOC2007_DEST/JPEGImages/000004.jpg: Predicted in 19.047000 milli-seconds.
car: 89%
car: 77%
truck: 49%
truck: 28%
car: 33%
car: 97%
car: 98%
car: 96%
car: 98%
car: 99%
car: 99%
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
6 : compute_capability = 700, cudnn_half = 0, GPU: Tesla V100-SXM2-32GB
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
22 route 21 12 -> 152 x 152 x 128
23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF
24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF
25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
26 route 24 -> 76 x 76 x 256
27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
53 route 52 25 -> 76 x 76 x 256
54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF
55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF
56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
57 route 55 -> 38 x 38 x 512
58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
84 route 83 56 -> 38 x 38 x 512
85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF
86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF
87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
88 route 86 -> 19 x 19 x1024
89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
103 route 102 87 -> 19 x 19 x1024
104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF
105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF
109 route 107 -> 19 x 19 x 512
110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF
111 route 107 -> 19 x 19 x 512
112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF
113 route 112 110 108 107 -> 19 x 19 x2048
114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF
115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF
118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
119 route 85 -> 38 x 38 x 512
120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
121 route 120 118 -> 38 x 38 x 512
122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF
128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
129 route 54 -> 76 x 76 x 256
130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
131 route 130 128 -> 76 x 76 x 256
132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
138 conv 30 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 30 0.089 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
140 route 136 -> 76 x 76 x 128
141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF
142 route 141 126 -> 38 x 38 x 512
143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
149 conv 30 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 30 0.044 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
151 route 147 -> 38 x 38 x 256
152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF
153 route 152 116 -> 19 x 19 x1024
154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
160 conv 30 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 30 0.022 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 127.294
avg_outputs = 1047336
Allocate additional workspace_size = 52.43 MB
Loading weights from backup/vechle_best.weights...Done! Loaded 162 layers from weights-file
Unable to init server: Could not connect: 拒绝连接
OpenCV exception: show_image_cv
%ºsh
#额外输出目标框的位置
darknet detector test cfg/vechle.data cfg/vechle.cfg backup/vechle_best.weights -ext_output /workspace/vechcle/VOCdevkit/VOC2007_DEST/JPEGImages/000004.jpg -thresh 0.25
net.optimized_memory = 0
mini_batch = 1, batch = 16, time_steps = 1, train = 0
Create CUDA-stream - 0
Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
seen 64, trained: 918 K-images (14 Kilo-batches_64)
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
/workspace/vechcle/VOCdevkit/VOC2007_DEST/JPEGImages/000004.jpg: Predicted in 19.375000 milli-seconds.
car: 89% (left_x: -0 top_y: 323 width: 17 height: 20)
car: 77% (left_x: 12 top_y: 309 width: 75 height: 56)
truck: 49% (left_x: 15 top_y: 310 width: 68 height: 53)
truck: 28% (left_x: 54 top_y: 307 width: 29 height: 22)
car: 33% (left_x: 78 top_y: 327 width: 13 height: 15)
car: 97% (left_x: 85 top_y: 326 width: 29 height: 23)
car: 98% (left_x: 108 top_y: 327 width: 35 height: 25)
car: 96% (left_x: 137 top_y: 322 width: 50 height: 36)
car: 98% (left_x: 173 top_y: 326 width: 78 height: 37)
car: 99% (left_x: 229 top_y: 329 width: 103 height: 45)
car: 99% (left_x: 362 top_y: 327 width: 134 height: 62)
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
0 : compute_capability = 700, cudnn_half = 0, GPU: Tesla V100-SXM2-32GB
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
22 route 21 12 -> 152 x 152 x 128
23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF
24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF
25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
26 route 24 -> 76 x 76 x 256
27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
53 route 52 25 -> 76 x 76 x 256
54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF
55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF
56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
57 route 55 -> 38 x 38 x 512
58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
84 route 83 56 -> 38 x 38 x 512
85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF
86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF
87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
88 route 86 -> 19 x 19 x1024
89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
103 route 102 87 -> 19 x 19 x1024
104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF
105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF
109 route 107 -> 19 x 19 x 512
110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF
111 route 107 -> 19 x 19 x 512
112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF
113 route 112 110 108 107 -> 19 x 19 x2048
114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF
115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF
118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
119 route 85 -> 38 x 38 x 512
120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
121 route 120 118 -> 38 x 38 x 512
122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF
128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
129 route 54 -> 76 x 76 x 256
130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
131 route 130 128 -> 76 x 76 x 256
132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
138 conv 30 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 30 0.089 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
140 route 136 -> 76 x 76 x 128
141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF
142 route 141 126 -> 38 x 38 x 512
143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
149 conv 30 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 30 0.044 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
151 route 147 -> 38 x 38 x 256
152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF
153 route 152 116 -> 19 x 19 x1024
154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
160 conv 30 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 30 0.022 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 127.294
avg_outputs = 1047336
Allocate additional workspace_size = 52.43 MB
Loading weights from backup/vechle_best.weights...Done! Loaded 162 layers from weights-file
Unable to init server: Could not connect: 拒绝连接
OpenCV exception: show_image_cv
%ºsh
#对视频进行测试并保存结果
darknet detector demo cfg/vechle.data cfg/vechle.cfg backup/vechle_best.weights test.mp4 -out_filename result.avi
%ºsh
#对摄像头
darknet detector demo cfg/vechle.data cfg/vechle.cfg backup/vechle_best.weights -c 0 -out_filename result.avi
%ºsh
#处理多张图片
darknet detector demo cfg/vechle.data cfg/vechle.cfg backup/vechle_best.weights -dont_show -ext_output < cfg/val.txt > result.txt
Process is interrupted.
%ºsh
#生成伪标签，可以用来将模型结果做为人工较正前的伪标签b
这个工作暂时也不做了，不难，主要是应用darknet 进行图片推理，然后获得结果，最后利用coco转voc中找码转成xml即可
UsageError: Line magic function `%ºsh` not found.
4.2 python对图片和视频的推理
4.2.1 图片推理
import os
import cv2
import numpy as np
import random
import sys
import infer
#os.chdir(os.path.dirname(os.path.abspath(__file__)))
# sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import darknet
class DetectImage:
def __init__(self, dataPath, configPath, weightPath, namesPath, gpu_id=0):
'''
:param metaPath: ***.data 存储各种参数
:param configPath: ***.cfg 网络结构文件
:param weightPath: ***.weights yolo的权重
:param namesPath: ***.names中的names路径，这里是便于读取使用
'''
#进行参数检验
if not os.path.exists(configPath):
raise(ValueError("Invalid config path {}".format(os.path.abspath(configPath))))
if not os.path.exists(weightPath):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(weightPath))))
if not os.path.exists(dataPath):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(dataPath))))
if not isinstance(gpu_id,int):
raise(ValueError("Invalid gpu id {}".format(gpu_id)))
# 设置gpu_id
darknet.set_gpu(gpu_id)
# 网络,各种参数，分类名称，分类对应的颜色,batch_size=1,单张图片进行处理
self.network, self.class_names, self.class_colors = darknet.load_network(
configPath,
dataPath,
weightPath,
batch_size=1)
def predict_image(self, image, thresh=0.25, is_show=False, save_path=''):
'''
:param image: cv2.imread 图像, darknet自己会对图像进行预处理
:param thresh: 置信度阈值, 其它阈值不变
:param is_show: 是否将画框之后的图像返回
:param save_path: 画框后的保存路径
:return: 返回1个矩阵
'''
# bgr->rgb
rgb_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# 获取图片大小，网络输入大小
height, width = rgb_img.shape[:2]
network_width = darknet.network_width(self.network)
network_height = darknet.network_height(self.network)
darknet_image = darknet.make_image(network_width, network_height, 3)
image_resized = cv2.resize(rgb_img, (network_width, network_height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(self.network, self.class_names, darknet_image, thresh=thresh)
darknet.free_image(darknet_image)
# image = darknet.draw_boxes(detections, image_resized, class_colors)
origin_image_detections=[]
for label, confidence, bbox in detections:
x,y,w,h = bbox
# 获取在原图中坐标
x *= width / network_width
w *= width / network_width
y *= height / network_height
h *= height / network_height
left,top,right,bottom=darknet.bbox2points([x,y,w,h])
origin_image_detections.append([label,confidence,[left,top,right,bottom]])
if is_show:
image = darknet.draw_boxes(origin_image_detections, rgb_img, self.class_colors)
bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# 保存图像
if save_path:
cv2.imwrite(save_path, bgr_img)
return image #返回画框的rgb图像
return origin_image_detections
# if __name__=="__main__":
# detect = DetectImage(dataPath=r'./cfg/vechcle.data',
# configPath=r'./cfg/vechcle.cfg',
# weightPath=r'./backup/vechcle_best.weights',
# namesPath=r'./cfg/vechcle.names',
# gpu_id=1)
# detections = detect.predict_image(image)
# darknet.print_detections(detections,True)
# image = cv2.imread(r'./data/car.jpg', -1)
# new_image=detect.predict_image(image, is_show=True,save_path='./data/pred.jpg')
# # detections = detect.predict_image(image)
# # darknet.print_detections(detections,True)
import os
import cv2
import numpy as np
import random
import sys
import infer
#os.chdir(os.path.dirname(os.path.abspath(__file__)))
# sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import darknet
import threading
class DetectImage:
_instance_lock = threading.Lock()
def __init__(self, dataPath, configPath, weightPath, namesPath, gpu_id=0):
'''
:param metaPath: ***.data 存储各种参数
:param configPath: ***.cfg 网络结构文件
:param weightPath: ***.weights yolo的权重
:param namesPath: ***.names中的names路径，这里是便于读取使用
'''
#进行参数检验
if not os.path.exists(configPath):
raise(ValueError("Invalid config path {}".format(os.path.abspath(configPath))))
if not os.path.exists(weightPath):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(weightPath))))
if not os.path.exists(dataPath):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(dataPath))))
if not isinstance(gpu_id,int):
raise(ValueError("Invalid gpu id {}".format(gpu_id)))
# 设置gpu_id
darknet.set_gpu(gpu_id)
# 网络,各种参数，分类名称，分类对应的颜色,batch_size=1,单张图片进行处理
self.network, self.class_names, self.class_colors = darknet.load_network(
configPath,
dataPath,
weightPath,
batch_size=1)
def __new__(cls, *args, **kwargs):
if not hasattr(cls, "__instance"):
with DetectImage._instance_lock:
if not hasattr(cls, "_instance"):
DetectImage._instance = super().__new__(cls)
return DetectImage._instance
def predict_image(self, image, thresh=0.25, is_show=False, save_path=''):
'''
:param image: cv2.imread 图像, darknet自己会对图像进行预处理
:param thresh: 置信度阈值, 其它阈值不变
:param is_show: 是否将画框之后的图像返回
:param save_path: 画框后的保存路径
:return: 返回1个矩阵
'''
# bgr->rgb
rgb_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# 获取图片大小，网络输入大小
height, width = rgb_img.shape[:2]
network_width = darknet.network_width(self.network)
network_height = darknet.network_height(self.network)
darknet_image = darknet.make_image(network_width, network_height, 3)
image_resized = cv2.resize(rgb_img, (network_width, network_height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(self.network, self.class_names, darknet_image, thresh=thresh)
darknet.free_image(darknet_image)
# image = darknet.draw_boxes(detections, image_resized, class_colors)
origin_image_detections=[]
for label, confidence, bbox in detections:
x,y,w,h = bbox
# 获取在原图中坐标
x *= width / network_width
w *= width / network_width
y *= height / network_height
h *= height / network_height
left,top,right,bottom=darknet.bbox2points([x,y,w,h])
origin_image_detections.append([label,confidence,[left,top,right,bottom]])
if is_show:
image = darknet.draw_boxes(origin_image_detections, rgb_img, self.class_colors)
bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# 保存图像
if save_path:
cv2.imwrite(save_path, bgr_img)
return image #返回画框的rgb图像
return origin_image_detections
detect = DetectImage(dataPath=r'./cfg/vechcle.data',
configPath=r'./cfg/vechcle.cfg',
weightPath=r'./backup/vechcle_best.weights',
namesPath=r'./cfg/vechcle.names',
gpu_id=1)
image = cv2.imread(r'./data/car.jpg', -1)
new_image=detect.predict_image(image, is_show=True,save_path='./data/pred2.jpg')
# detections = detect.predict_image(image)
# darknet.print_detections(detections,True)
!pwd
/workspace/vechcle
import matplotlib.pyplot as plt
plt.figure(figsize=(30,15))
plt.imshow(new_image)
plt.show()
4.2.2 视频推理
import infer
import cv2
import darknet
import os
import time
import random
class DetectVideo:
def __init__(self,args):
self.args=args
if not isinstance(self.args,edict):
print("""args is not a valid edict,please check:\n
args.input type:str default=0\n
args.out_filename type:str default= \n
args.weights,default=yolov4.weights \n
args.dont_show windown inference display. For headless systems \n
args.ext_output display bbox coordinates of detected objects \n
args.config_file "./cfg/yolov4.cfg path to config file \n
args.data_file ./cfg/coco.data path to data file \n
args.thresh 0.25 remove detections with confidence below this value\n
args.gpu_id 1 int select use which gpu do inferece""")
else:
self.check_arguments_errors(self.args)
# 设置gpu_id
darknet.set_gpu(self.args.gpu_id)
self.network, self.class_names, self.class_colors = darknet.load_network(
self.args.config_file,
self.args.data_file,
self.args.weights,
batch_size=1
)
self.width = darknet.network_width(self.network)
self.height = darknet.network_height(self.network)
input_path = self.str2int(self.args.input)
self.cap = cv2.VideoCapture(input_path)
def str2int(self,video_path):
"""
argparse returns and string althout webcam uses int (0, 1 ...)
Cast to int if needed
"""
try:
return int(video_path)
except ValueError:
return video_path
def check_arguments_errors(self,args):
assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
if not os.path.exists(args.config_file):
raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
if not os.path.exists(args.weights):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
if not os.path.exists(args.data_file):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file))))
if self.str2int(args.input) == str and not os.path.exists(args.input):
raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input))))
def set_saved_video(self,input_video, output_video, size):
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
fps = int(input_video.get(cv2.CAP_PROP_FPS))
video = cv2.VideoWriter(output_video, fourcc, fps, size)
return video
def video_capture(self):
i=0
while self.cap.isOpened():
ret, frame = self.cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
height, width = frame_rgb.shape[:2]
if i==0:
video = self.set_saved_video(self.cap, self.args.out_filename, (width, height))
i+=1
frame_resized = cv2.resize(frame_rgb, (self.width, self.height),
interpolation=cv2.INTER_LINEAR)
img_for_detect = darknet.make_image(self.width, self.height, 3)
darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
prev_time = time.time()
detections = darknet.detect_image(self.network, self.class_names, img_for_detect, thresh=self.args.thresh)
darknet.free_image(img_for_detect)
fps = int(1/(time.time() - prev_time))
print("FPS: {}".format(fps))
origin_image_detections=[]
for label, confidence, bbox in detections:
x,y,w,h = bbox
# 获取在原图中坐标
x *= width / self.width
w *= width / self.width
y *= height / self.height
h *= height / self.height
left,top,right,bottom=darknet.bbox2points([x,y,w,h])
origin_image_detections.append([label,confidence,[left,top,right,bottom]])
image = darknet.draw_boxes(origin_image_detections, frame_rgb, self.class_colors)
bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if self.args.out_filename is not None:
video.write(bgr_img)
if not self.args.dont_show:
cv2.imshow('Inference', bgr_img)
if cv2.waitKey(fps) == 27:
break
self.cap.release()
video.release()
cv2.destroyAllWindows()
from easydict import EasyDict as edict
# from infer.detect_video import DetectVideo
args=edict()
args.input='./data/road.mp4'
args.out_filename='./data/pred.mp4'
args.weights='./backup/vechcle_best.weights'
args.dont_show=True
args.ext_output=True
args.config_file='./cfg/vechcle.cfg'
args.data_file='./cfg/vechcle.data'
args.thresh=0.25
args.gpu_id=1
detectv = DetectVideo(args)
detectv.video_capture()
FPS: 48
FPS: 47
FPS: 50
FPS: 50
FPS: 49
FPS: 50
FPS: 50
FPS: 47
FPS: 50
FPS: 48
FPS: 47
FPS: 52
FPS: 52
FPS: 51
FPS: 51
FPS: 50
FPS: 50
FPS: 53
FPS: 52
FPS: 51
FPS: 51
FPS: 51
FPS: 50
FPS: 52
FPS: 52
FPS: 51
FPS: 51
FPS: 50
FPS: 50
FPS: 52
FPS: 50
FPS: 52
FPS: 52
FPS: 51
FPS: 50
FPS: 51
FPS: 56
FPS: 56
还有一种并行的方法，没有搞明白暂时，是这个样子
!python darknet_video.py ，以下代码为darknet_video.py中内容，但是在jupyter中没跑通
import random
import infer
import os
import cv2
import time
import darknet
import argparse
from threading import Thread, enumerate
from queue import Queue
from easydict import EasyDict as edict
def parser():
parser = argparse.ArgumentParser(description="YOLO Object Detection")
parser.add_argument("--input", type=str, default=0,
help="video source. If empty, uses webcam 0 stream")
parser.add_argument("--out_filename", type=str, default="",
help="inference video name. Not saved if empty")
parser.add_argument("--weights", default="yolov4.weights",
help="yolo weights path")
parser.add_argument("--dont_show", action='store_true',
help="windown inference display. For headless systems")
parser.add_argument("--ext_output", action='store_true',
help="display bbox coordinates of detected objects")
parser.add_argument("--config_file", default="./cfg/yolov4.cfg",
help="path to config file")
parser.add_argument("--data_file", default="./cfg/coco.data",
help="path to data file")
parser.add_argument("--thresh", type=float, default=.25,
help="remove detections with confidence below this value")
return parser.parse_args()
def str2int(video_path):
"""
argparse returns and string althout webcam uses int (0, 1 ...)
Cast to int if needed
"""
try:
return int(video_path)
except ValueError:
return video_path
def check_arguments_errors(args):
assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
if not os.path.exists(args.config_file):
raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
if not os.path.exists(args.weights):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
if not os.path.exists(args.data_file):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file))))
if str2int(args.input) == str and not os.path.exists(args.input):
raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input))))
def set_saved_video(input_video, output_video, size):
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
fps = int(input_video.get(cv2.CAP_PROP_FPS))
# print(f"fps{fps} size {size}")
video = cv2.VideoWriter(output_video, fourcc, fps, size)
return video
def video_capture(frame_queue, darknet_image_queue):
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
# frame_queue.put(frame_resized)
frame_queue.put(frame_rgb)
img_for_detect = darknet.make_image(width, height, 3)
darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
darknet_image_queue.put(img_for_detect)
cap.release()
def inference(darknet_image_queue, detections_queue, fps_queue):
while cap.isOpened():
darknet_image = darknet_image_queue.get()
prev_time = time.time()
detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh)
origin_image_detections=[]
for label, confidence, bbox in detections:
x,y,w,h = bbox
# # 获取在原图中坐标
x *= img_width / width
w *= img_width / width
y *= img_height / height
h *= img_height / height
left,top,right,bottom=darknet.bbox2points([x,y,w,h])
origin_image_detections.append([label,confidence,[left,top,right,bottom]])
detections_queue.put(origin_image_detections)
fps = int(1/(time.time() - prev_time))
fps_queue.put(fps)
print("FPS: {}".format(fps))
# darknet.print_detections(detections, args.ext_output)
darknet.free_image(darknet_image)
cap.release()
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (int(img_width), int(img_height)))
while cap.isOpened():
frame_rgb = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
if frame_rgb is not None:
image = darknet.draw_boxes(detections, frame_rgb, class_colors)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.out_filename is not None:
# print("drawing ...,img.shape",image.shape)
video.write(image)
if not args.dont_show:
cv2.imshow('Inference', image)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
frame_queue = Queue() #存放resized 后图片
darknet_image_queue = Queue(maxsize=1)
detections_queue = Queue(maxsize=1)
fps_queue = Queue(maxsize=1)
# args = parser()
args=edict()
args.input='./data/road.mp4'
args.out_filename='./data/pred_thred.mp4'
args.weights='./backup/vechcle_best.weights'
args.dont_show=True
args.ext_output=True
args.config_file='./cfg/vechcle.cfg'
args.data_file='./cfg/vechcle.data'
args.thresh=0.25
args.gpu_id=1
# args = parser()
check_arguments_errors(args)
darknet.set_gpu(args.gpu_id)
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=1
)
width = darknet.network_width(network)
height = darknet.network_height(network)
input_path = str2int(args.input)
cap = cv2.VideoCapture(input_path)
img_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH )
img_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start()
Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start()
Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start()
fps25 size (1280, 720)
frame_queue = Queue() #存放resized 后图片
darknet_image_queue = Queue(maxsize=1)
detections_queue = Queue(maxsize=1)
fps_queue = Queue(maxsize=1)
# args = parser()
args=edict()
args.input='./data/road.mp4'
args.out_filename='./data/pred_thred.mp4'
args.weights='./backup/vechcle_best.weights'
args.dont_show=True
args.ext_output=True
args.config_file='./cfg/vechcle.cfg'
args.data_file='./cfg/vechcle.data'
args.thresh=0.25
args.gpu_id=1
# args = parser()
check_arguments_errors(args)
darknet.set_gpu(args.gpu_id)
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=1
)
width = darknet.network_width(network)
height = darknet.network_height(network)
input_path = str2int(args.input)
cap = cv2.VideoCapture(input_path)
img_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH )
img_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start()
Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start()
Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start()
FPS: 11
FPS: 34
FPS: 26
FPS: 48
FPS: 37
FPS: 37
FPS: 47
FPS: 47
FPS: 45
FPS: 48
FPS: 50
FPS: 50
FPS: 46
FPS: 54
FPS: 55
FPS: 48
FPS: 51
FPS: 51
FPS: 55
FPS: 57
FPS: 56
FPS: 57
FPS: 54
FPS: 57
FPS: 54
FPS: 57
FPS: 57
FPS: 57
FPS: 53
FPS: 59
FPS: 55
FPS: 51
FPS: 47
FPS: 53
FPS: 55
FPS: 54
FPS: 54
fps25 size (1280, 720)
FPS: 52
FPS: 56
FPS: 57
FPS: 57
FPS: 54
FPS: 56
FPS: 56
FPS: 55
FPS: 54
FPS: 55
FPS: 57
FPS: 55
FPS: 55
FPS: 56
FPS: 56
FPS: 56
FPS: 57
FPS: 57
FPS: 56
FPS: 56
FPS: 57
FPS: 57
FPS: 56
FPS: 57
FPS: 56
FPS: 55
FPS: 53
FPS: 54
FPS: 52
FPS: 54
FPS: 54
FPS: 53
FPS: 54
FPS: 55
FPS: 53
FPS: 53
FPS: 55
4.2.3 显示视频
from IPython.display import clear_output, display, HTML
from PIL import Image
import matplotlib.pyplot as plt
import time
import cv2
import base64
current_time = 0
# 图像处理函数
def processImg(img):
# 画出一个框
# cv2.rectangle(img, (500, 300), (800, 400), (0, 0, 255), 5, 1, 0)
# 上下翻转
# img= cv2.flip(img, 0)
# 显示FPS
global current_time
if current_time == 0:
current_time = time.time()
else:
last_time = current_time
current_time = time.time()
fps = 1. / (current_time - last_time)
text = "FPS: %d" % int(fps)
cv2.putText(img, text , (0,100), cv2.FONT_HERSHEY_TRIPLEX, 3.65, (255, 0, 0), 2)
# img = cv2.resize(img,(1080,1080))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
def arrayShow(imageArray):
# ret, png = cv2.imencode('.png', imageArray)
# encoded = base64.b64encode(png)
# return Image(data=encoded.decode('ascii'))
return Image.fromarray(imageArray)
video = cv2.VideoCapture("./data/road.mp4")
small=1
while(True):
try:
clear_output(wait=True)
ret, frame = video.read()
if not ret:
break
lines, columns, _ = frame.shape
frame = processImg(frame)
frame = cv2.resize(frame, (int(columns / small), int(lines / small)))
img = arrayShow(frame)
display(img)
# 控制帧率
time.sleep(0.02)
except KeyboardInterrupt:
video.release()
5、模型性能统计测试
有12个模型完成训练，其中有部分也不收敛，可以看训练模型结果保存的png图片,模型测试顺序就不按什么顺序了
5.1 yolov4模型测试方法一
以下为map@iou=0.50.5的结果
%%bash
darknet detector map cfg/vechcle.data cfg/yolov4_vechcle.cfg backup/yolov4_vechcle_best.weights
net.optimized_memory = 0
mini_batch = 1, batch = 16, time_steps = 1, train = 0
Create CUDA-stream - 0
Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
seen 64, trained: 424 K-images (6 Kilo-batches_64)
calculation mAP (mean average precision)...
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
detections_count = 36911, unique_truth_count = 4058
class_id = 0, name = bicycle, ap = 64.76% (TP = 230, FP = 99)
class_id = 1, name = bus, ap = 86.69% (TP = 270, FP = 67)
class_id = 2, name = car, ap = 74.02% (TP = 1739, FP = 1002)
class_id = 3, name = motorbike, ap = 73.81% (TP = 350, FP = 140)
class_id = 4, name = truck, ap = 64.48% (TP = 326, FP = 263)
for conf_thresh = 0.25, precision = 0.65, recall = 0.72, F1-score = 0.68
for conf_thresh = 0.25, TP = 2915, FP = 1571, FN = 1143, average IoU = 51.59 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.727514, or 72.75 %
Set -points flag:
`-points 101` for MS COCO
`-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data)
`-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
0 : compute_capability = 700, cudnn_half = 0, GPU: Tesla V100-SXM2-32GB
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
22 route 21 12 -> 152 x 152 x 128
23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF
24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF
25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
26 route 24 -> 76 x 76 x 256
27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
53 route 52 25 -> 76 x 76 x 256
54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF
55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF
56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
57 route 55 -> 38 x 38 x 512
58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
84 route 83 56 -> 38 x 38 x 512
85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF
86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF
87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
88 route 86 -> 19 x 19 x1024
89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
103 route 102 87 -> 19 x 19 x1024
104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF
105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF
109 route 107 -> 19 x 19 x 512
110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF
111 route 107 -> 19 x 19 x 512
112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF
113 route 112 110 108 107 -> 19 x 19 x2048
114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF
115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF
118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
119 route 85 -> 38 x 38 x 512
120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
121 route 120 118 -> 38 x 38 x 512
122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF
128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
129 route 54 -> 76 x 76 x 256
130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
131 route 130 128 -> 76 x 76 x 256
132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
138 conv 30 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 30 0.089 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
140 route 136 -> 76 x 76 x 128
141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF
142 route 141 126 -> 38 x 38 x 512
143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
149 conv 30 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 30 0.044 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
151 route 147 -> 38 x 38 x 256
152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF
153 route 152 116 -> 19 x 19 x1024
154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
160 conv 30 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 30 0.022 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 127.294
avg_outputs = 1047336
Allocate additional workspace_size = 52.43 MB
Loading weights from backup/yolov4_vechcle_best.weights...Done! Loaded 162 layers from weights-file
1032Total Detection Time: 21 Seconds
要测map@iou=0.75
%%bash
darknet detector map cfg/vechcle.data cfg/yolov4_vechcle.cfg backup/yolov4_vechcle_best.weights -iou_thresh 0.75
net.optimized_memory = 0
mini_batch = 1, batch = 16, time_steps = 1, train = 0
Create CUDA-stream - 0
Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
seen 64, trained: 424 K-images (6 Kilo-batches_64)
calculation mAP (mean average precision)...
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
detections_count = 36911, unique_truth_count = 4058
class_id = 0, name = bicycle, ap = 29.88% (TP = 132, FP = 197)
class_id = 1, name = bus, ap = 71.45% (TP = 241, FP = 96)
class_id = 2, name = car, ap = 44.90% (TP = 1221, FP = 1520)
class_id = 3, name = motorbike, ap = 39.21% (TP = 221, FP = 269)
class_id = 4, name = truck, ap = 37.34% (TP = 239, FP = 350)
for conf_thresh = 0.25, precision = 0.46, recall = 0.51, F1-score = 0.48
for conf_thresh = 0.25, TP = 2054, FP = 2432, FN = 2004, average IoU = 39.15 %
IoU threshold = 75 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.75) = 0.445590, or 44.56 %
Set -points flag:
`-points 101` for MS COCO
`-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data)
`-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
0 : compute_capability = 700, cudnn_half = 0, GPU: Tesla V100-SXM2-32GB
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
22 route 21 12 -> 152 x 152 x 128
23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF
24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF
25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
26 route 24 -> 76 x 76 x 256
27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
53 route 52 25 -> 76 x 76 x 256
54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF
55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF
56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
57 route 55 -> 38 x 38 x 512
58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
84 route 83 56 -> 38 x 38 x 512
85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF
86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF
87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
88 route 86 -> 19 x 19 x1024
89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
103 route 102 87 -> 19 x 19 x1024
104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF
105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF
109 route 107 -> 19 x 19 x 512
110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF
111 route 107 -> 19 x 19 x 512
112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF
113 route 112 110 108 107 -> 19 x 19 x2048
114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF
115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF
118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
119 route 85 -> 38 x 38 x 512
120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
121 route 120 118 -> 38 x 38 x 512
122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF
128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
129 route 54 -> 76 x 76 x 256
130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
131 route 130 128 -> 76 x 76 x 256
132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
138 conv 30 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 30 0.089 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
140 route 136 -> 76 x 76 x 128
141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF
142 route 141 126 -> 38 x 38 x 512
143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
149 conv 30 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 30 0.044 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
151 route 147 -> 38 x 38 x 256
152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF
153 route 152 116 -> 19 x 19 x1024
154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
160 conv 30 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 30 0.022 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 127.294
avg_outputs = 1047336
Allocate additional workspace_size = 52.43 MB
Loading weights from backup/yolov4_vechcle_best.weights...Done! Loaded 162 layers from weights-file
1032Total Detection Time: 21 Seconds
5.2 yolov4模型性能测试方法二
新建results文件夹然后执行以下命令,对验证集批量产生结果：
%%bash
darknet detector valid cfg/vechcle.data cfg/yolov4_vechcle.cfg backup/yolov4_vechcle_best.weights
net.optimized_memory = 0
mini_batch = 1, batch = 16, time_steps = 1, train = 0
Create CUDA-stream - 0
Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
seen 64, trained: 424 K-images (6 Kilo-batches_64)
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
results: Using default 'results'
0 : compute_capability = 700, cudnn_half = 0, GPU: Tesla V100-SXM2-32GB
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
22 route 21 12 -> 152 x 152 x 128
23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF
24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF
25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
26 route 24 -> 76 x 76 x 256
27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF
52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF
53 route 52 25 -> 76 x 76 x 256
54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF
55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF
56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
57 route 55 -> 38 x 38 x 512
58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF
83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF
84 route 83 56 -> 38 x 38 x 512
85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF
86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF
87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
88 route 86 -> 19 x 19 x1024
89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF
102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF
103 route 102 87 -> 19 x 19 x1024
104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF
105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF
109 route 107 -> 19 x 19 x 512
110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF
111 route 107 -> 19 x 19 x 512
112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF
113 route 112 110 108 107 -> 19 x 19 x2048
114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF
115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF
118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256
119 route 85 -> 38 x 38 x 512
120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
121 route 120 118 -> 38 x 38 x 512
122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF
128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128
129 route 54 -> 76 x 76 x 256
130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
131 route 130 128 -> 76 x 76 x 256
132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF
137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF
138 conv 30 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 30 0.089 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
140 route 136 -> 76 x 76 x 128
141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF
142 route 141 126 -> 38 x 38 x 512
143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF
148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF
149 conv 30 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 30 0.044 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
151 route 147 -> 38 x 38 x 256
152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF
153 route 152 116 -> 19 x 19 x1024
154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF
159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF
160 conv 30 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 30 0.022 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 127.294
avg_outputs = 1047336
Allocate additional workspace_size = 52.43 MB
Loading weights from backup/yolov4_vechcle_best.weights...Done! Loaded 162 layers from weights-file
Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005
eval: Using default 'voc'
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Total Detection Time: 42.000000 Seconds
%%bash
ls results
comp4_det_test_bicycle.txt
comp4_det_test_bus.txt
comp4_det_test_car.txt
comp4_det_test_motorbike.txt
comp4_det_test_truck.txt
%%bash
head -n 15 ./results/comp4_det_test_bicycle.txt
000000401980 0.839656 1.000000 207.924377 362.778381 427.000000
000000401980 0.617458 343.060028 215.706848 640.000000 427.000000
000000401980 0.137739 365.956360 266.341797 632.603455 427.000000
000000401980 0.048878 1.000000 113.423553 373.908722 427.000000
000000401980 0.027138 258.326660 183.985229 640.000000 427.000000
000000401980 0.005769 362.092041 282.595978 558.476379 422.788727
000000401980 0.005717 399.438232 134.345291 635.504150 427.000000
000000401980 0.004526 379.546814 323.268311 616.657593 427.000000
000000401980 0.002933 381.449188 354.378296 602.514404 427.000000
000000401980 0.002256 371.549042 362.315216 533.911743 427.000000
000000401980 0.002217 345.463776 288.028381 503.355743 419.107727
000000401980 0.001431 357.871887 364.965546 492.497253 427.000000
000000401980 0.001198 344.290649 252.155457 584.557373 409.533447
000000401980 0.001176 396.900513 374.411926 597.720337 426.435547
000000401980 0.001167 448.817688 215.300049 640.000000 427.000000
以上结果为：文件名 置信度 x y w h
然后将reval_voc_py3.py 和 voc_eval_py3.py放到和results同一级，具体内容要看源代码，然后执行以下代码，当然，我们的数据放到多个文件中，所以呢想要用这份代码需要改一下，暂时没必要
%%bash
python reval_voc_py.py --voc_dir VOCdevkit --year 2007 --image_set test --class ./cfg/vechcle.names --output_dir test
–voc_dir为数据集路径
年份为数据集文件夹时间 VOC2012
验证集文件名test.txt \VOC2017\ImageSets\Main
类名文件 my_Data.names
输出文件夹名 test
要自己新建test文件夹
获重结果后可以画Pr曲线，具体参见https://blog.csdn.net/weixin_45702256/article/details/119321994 这里就不再多做了，有需要再写
6、Anchor Box先验框聚类分析与修改
这个工作是对于有些数据，目标大小分部特殊，可以进行先验框计算，然后再进行操作
6.1 使用k-means聚类获得自己数据集的先验框大小
%%bash
darknet detector calc_anchors cfg/vechcle.data -num_of_clusters 9 -width 608 -height 608
IOPub data rate exceeded.
The Jupyter server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--ServerApp.iopub_data_rate_limit`.
Current values:
ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)
ServerApp.rate_limit_window=3.0 (secs)
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
以上命令可以放到shell中执行，结果大致如下，聚类结果保存在anchors.txt中,还有一个txt文档是counters_per_class.txt是用来保存每个分类有多少个框的,calc_anchors 计算是采用随机数初始的, 每次计算出的结果不太一样,可以多计算几次,从中选一个
root@884f85fe676b:/workspace/yolo_demo# darknet detector calc_anchors cfg/vechcle.data -num_of_clusters 9 -width 608 -height 608
CUDA-version: 10010 (11020), cuDNN: 7.6.5, GPU count: 8
OpenCV version: 4.5.0
num_of_clusters = 9, width = 608, height = 608
read labels from 24710 images
loaded image: 24710 box: 83514
all loaded.
calculating k-means++ ...
iterations = 148
counters_per_class = 8488, 7101, 48015, 10024, 9886
avg IoU = 60.70 %
Saving anchors to the file: anchors.txt
anchors = 21, 20, 48, 51, 114, 71, 75,150, 183,141, 174,287, 378,205, 342,387, 532,467
也可以通过python脚本来获取聚类值,gen_anchors.py有1个要修改的地方 第17~20行，改成自己的数值（416，608，832），本代码运行时间较长
%%bash
python gen_anchors.py -filelist cfg/train.txt -output_dir ./ -num_clusters 9
iter 1: dists = 606424.0129503558
iter 2: dists = 28501.70724020651
iter 3: dists = 9202.088097536196
iter 4: dists = 7301.728386852483
iter 5: dists = 6224.9412334696135
iter 6: dists = 5279.722984555354
iter 7: dists = 5562.663534725808
iter 8: dists = 4931.476816589927
iter 9: dists = 4582.141052156835
iter 10: dists = 4237.761034128802
iter 11: dists = 3811.5987488219857
iter 12: dists = 3385.8211120126666
iter 13: dists = 3128.362208530587
iter 14: dists = 3027.810199984304
iter 15: dists = 2944.035280637849
iter 16: dists = 2817.7684800290976
iter 17: dists = 2704.5393944211996
iter 18: dists = 2629.978295207829
iter 19: dists = 2575.2117409453163
iter 20: dists = 2421.31764750135
iter 21: dists = 2314.0972249341444
iter 22: dists = 2350.7272863928665
iter 23: dists = 2205.09210862319
iter 24: dists = 2134.9107794899633
iter 25: dists = 2104.1593729091696
iter 26: dists = 1940.8498872758787
iter 27: dists = 1834.6228507632038
iter 28: dists = 1811.5731609054133
iter 29: dists = 1627.7702032853927
iter 30: dists = 1531.1355089902463
iter 31: dists = 1409.230196049714
iter 32: dists = 1331.010186032567
iter 33: dists = 1208.6370858551452
iter 34: dists = 1123.245206224764
iter 35: dists = 1161.68323373432
iter 36: dists = 1120.3508068951528
iter 37: dists = 1008.2834759048279
iter 38: dists = 963.0480960732043
iter 39: dists = 950.378881907364
iter 40: dists = 831.754848143817
iter 41: dists = 686.1207305738013
iter 42: dists = 694.7501987488523
iter 43: dists = 702.9238748781272
iter 44: dists = 659.3914386177174
iter 45: dists = 633.547019651719
iter 46: dists = 640.8125997364422
iter 47: dists = 683.8596404460656
iter 48: dists = 687.8244288910233
iter 49: dists = 632.2069728745607
iter 50: dists = 601.268527901974
iter 51: dists = 609.7783036473509
iter 52: dists = 540.079659477649
iter 53: dists = 543.0395995298043
iter 54: dists = 495.1369586120734
iter 55: dists = 448.9205076839092
iter 56: dists = 451.9265305583567
iter 57: dists = 445.8676191578089
iter 58: dists = 402.46432092324363
iter 59: dists = 410.36114650634823
iter 60: dists = 419.39735795228376
iter 61: dists = 403.08377988541355
iter 62: dists = 412.2322869786506
iter 63: dists = 399.7808853033867
iter 64: dists = 381.6629861380409
iter 65: dists = 346.68236976678884
iter 66: dists = 331.25112404048065
iter 67: dists = 398.7653146460294
iter 68: dists = 393.37940779976736
iter 69: dists = 361.70151584083834
iter 70: dists = 406.57577465581545
iter 71: dists = 403.5997123802948
iter 72: dists = 340.94629204413224
iter 73: dists = 301.86688158653806
iter 74: dists = 257.1864677198275
iter 75: dists = 259.1410070090174
iter 76: dists = 240.5869313623183
iter 77: dists = 186.85374469748768
iter 78: dists = 175.38548852401078
iter 79: dists = 143.6900990340137
iter 80: dists = 110.76006074764805
iter 81: dists = 83.22622649077756
iter 82: dists = 55.18584562380896
iter 83: dists = 59.80965602938752
iter 84: dists = 77.19873303640345
iter 85: dists = 54.78921305941409
iter 86: dists = 54.67409173222138
iter 87: dists = 42.90874525206753
iter 88: dists = 40.227378132718876
iter 89: dists = 29.68499873332845
iter 90: dists = 18.584079634494078
iter 91: dists = 26.92546517042183
iter 92: dists = 35.22220507457459
iter 93: dists = 24.537504581685816
iter 94: dists = 36.78372233252653
iter 95: dists = 34.441230795207126
iter 96: dists = 28.150615086656074
iter 97: dists = 35.22806850106039
iter 98: dists = 40.16756408945783
iter 99: dists = 38.86809583100122
iter 100: dists = 39.63676909922949
iter 101: dists = 46.93281184950921
iter 102: dists = 46.37818424940942
iter 103: dists = 49.79067540958222
iter 104: dists = 36.70871166407363
iter 105: dists = 42.78608325243664
iter 106: dists = 44.13492693454714
iter 107: dists = 51.63791119384016
iter 108: dists = 44.10700932769693
iter 109: dists = 36.93207799817509
iter 110: dists = 31.51401685953906
iter 111: dists = 25.365938403004666
iter 112: dists = 30.2241754040899
iter 113: dists = 33.54497956233244
iter 114: dists = 51.8453403055046
iter 115: dists = 40.52132999542395
iter 116: dists = 46.43421406384323
iter 117: dists = 26.20199740820365
iter 118: dists = 28.371169115678182
iter 119: dists = 51.680930634064644
iter 120: dists = 27.613150324391142
iter 121: dists = 20.0054051188211
iter 122: dists = 15.713291231280182
iter 123: dists = 13.88971458665659
iter 124: dists = 12.134412851153034
iter 125: dists = 8.45830337535558
iter 126: dists = 7.339011068590554
iter 127: dists = 3.9050840814664607
iter 128: dists = 3.0161374966758867
iter 129: dists = 6.812149812531408
iter 130: dists = 9.321844914262861
iter 131: dists = 13.27738102866969
iter 132: dists = 17.73013320209525
iter 133: dists = 14.824107393686482
iter 134: dists = 18.203157425063424
iter 135: dists = 12.553729764703663
iter 136: dists = 9.403500749277438
iter 137: dists = 3.2910376110133313
Centroids = [[0.83081278 0.81635949]
[0.03474245 0.03378914]
[0.21025277 0.12803264]
[0.11241804 0.21235076]
[0.22844756 0.35405258]
[0.08352834 0.08000769]
[0.73821317 0.48723731]
[0.4138531 0.56776151]
[0.45051463 0.24539772]]
Anchors = [[ 0.66010657 0.64199361]
[ 1.58703849 1.52014608]
[ 2.13594285 4.03466442]
[ 3.99480269 2.43262014]
[ 4.34050358 6.72699893]
[ 7.86320882 10.78746868]
[ 8.55977798 4.66255677]
[14.02605028 9.25750898]
[15.78544279 15.51083033]]
centroids.shape (9, 2)
结果保存在anchors9.txt，内容是：
0.66,0.64, 1.59,1.52, 2.14,4.03, 3.99,2.43, 4.34,6.73, 7.86,10.79, 8.56,4.66, 14.03,9.26, 15.79,15.51
0.607339
第一行是anchors,使用时乘以32就可以拿到cfg中使用
第二行是iou平均值
6.2 修改cfg中anchors大小，然后进行重新训练
cfg文件中搜anchors可以查看。
总结
我们把整个文档做为使用darknet的教程,以下大概看一下文件结构
%%bash
tree -L 1 ./
./
├── anchors9.txt
├── anchors.txt
├── backup
├── cfg
├── chart_enetb0_vechcle.png
├── chart.png
├── chart_yolov3_tiny_vechcle.png
├── chart_yolov3_vechcle_mosaic.png
├── chart_yolov3_vechcle.png
├── chart_yolov4_csp_swish_vechcle.png
├── chart_yolov4_csp_vechcle.png
├── chart_yolov4_csp_x_swish_vechcle.png
├── chart_yolov4_p5_vechcle.png
├── chart_yolov4_p6_vechcle.png
├── chart_yolov4_tiny_vechcle.png
├── chart_yolov4_vechcle.png
├── chart_yolov4x_mish_vechcle.png
├── COCO
├── core.105601
├── core.122098
├── core.123505
├── core.156773
├── core.159716
├── core.176745
├── core.23172
├── core.33626
├── core.60037
├── core.62810
├── counters_per_class.txt
├── darknet_video.py
├── data
├── data_prepare.ipynb
├── gen_anchors.py
├── infer
├── logs
├── model_prepare.ipynb
├── results
├── reval_voc_py3.py
├── train_yolov3_enet.sh
├── train_yolov3_mosaic.sh
├── train_yolov3.sh
├── train_yolov3_tiny.sh
├── train_yolov4_csp.sh
├── train_yolov4_csp_swish.sh
├── train_yolov4_csp_x_swish.sh
├── train_yolov4_p5.sh
├── train_yolov4_p6.sh
├── train_yolov4.sh
├── train_yolov4_tiny.sh
├── train_yolov4x_mish.sh
├── VOCdevkit
└── voc_eval_py3.py
8 directories, 44 files
core文件大概是数据增强时的文件
%%bash
tree -L 1 ./data
./data
├── bus_station.mp4
├── car.jpg
├── labels
├── pred1.jpg
├── pred2.jpg
├── pred_bus_station.mp4
├── pred.jpg
├── pred.mp4
├── pred_road.mp4
├── pred_thred.mp4
└── road.mp4
1 directory, 10 files
%%bash
tree -L 1 ./cfg
./cfg
├── darknet53.conv.74
├── enetb0-coco.conv.132
├── enetb0_vechcle.cfg
├── libdarknet.so
├── train.txt
├── val.txt
├── vechcle.data
├── vechcle.names
├── yolov3-tiny.conv.11
├── yolov3_tiny_vechcle.cfg
├── yolov3_vechcle.cfg
├── yolov3_vechcle_mosaic.cfg
├── yolov4.conv.137
├── yolov4-csp.conv.142
├── yolov4-csp-swish.conv.164
├── yolov4_csp_swish_vechcle.cfg
├── yolov4_csp_vechcle.cfg
├── yolov4-csp-x-swish.conv.192
├── yolov4_csp_x_swish_vechcle.cfg
├── yolov4-p5.conv.232
├── yolov4_p5_vechcle.cfg
├── yolov4-p6.conv.289
├── yolov4_p6_vechcle.cfg
├── yolov4-tiny.conv.29
├── yolov4_tiny_vechcle.cfg
├── yolov4_vechcle.cfg
├── yolov4x-mish.conv.166
└── yolov4x_mish_vechcle.cfg
0 directories, 28 files
%%bash
tree -L 1 ./logs
./logs
├── yolov3_enet.log
├── yolov3.log
├── yolov3_mosaic.log
├── yolov3_tiny.log
├── yolov4_csp.log
├── yolov4_csp_swish.log
├── yolov4_csp_x_swish.log
├── yolov4.log
├── yolov4_p5.log
├── yolov4_p6.log
├── yolov4_tiny.log
└── yolov4x_mish.log
0 directories, 12 files
为了做为一个项目文件，我将会把所用没必要的文件删除如core开头的文件，还有训练的模型中非best,logs中的文件也就删了。为了代码能直接跑通，数据集不准备删除，无非大一些，保存到移动硬盘即可，
coco有20G,VOC有27个G,感觉也没必要，还是处理一下，coco,voc呢全删,想用就解码跑一下代码好了
版权声明：本文为CSDN博主「牛andmore牛」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/u011119817/article/details/122454088
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