[object_detect]使用MobileNetSSD进行对象检测
使用MobileNetSSD进行对象检测
1.单帧图片识别
- object_detection.py
# 导入必要的包
import numpy as np
import argparse
import cv2
# 构造参数 parse 并解析参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-p", "--prototxt", required=True,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# 初始化 MobileNet SSD 训练的类标签列表
# 检测，然后为每个类生成一组边界框颜色
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# 从磁盘加载我们的序列化模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
# 加载输入图像并为图像构造一个输入 blob
# 将大小调整为固定的 300x300 像素，然后对其进行标准化
#（注意：标准化是通过 MobileNet SSD 完成执行的
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
# 通过网络传递blob并获得检测
# 预测
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()
# 循环检测
for i in np.arange(0, detections.shape[2]):
# 提取与相关的置信度（即概率）
confidence = detections[0, 0, i, 2]
# 通过确保置信度大于最小置信度来过滤无效检测
if confidence > args["confidence"]:
# 从类标签detections中提取索引，
# 然后计算物体边界框的 (x, y) 坐标
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# 显示预测结果
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
print("[INFO] {}".format(label))
cv2.rectangle(image, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(image, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# 显示输出图像
cv2.imshow("Output", image)
cv2.waitKey(0)
- 调用方法:
# 用法
python object_detection.py --image images/example_01.jpg --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
- 测试效果
2.视频流实时检测对象
- real_time_object_detection.py
# 导入必要的包
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
# 构造参数 parse 并解析参数
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# 初始化 MobileNet SSD 训练的类标签列表
# 检测，然后为每个类生成一组边界框颜色
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# 从磁盘加载我们的序列化模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
# 初始化视频流，允许摄像机传感器预加载，
# 并初始化 FPS 计数器
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()
# 循环读取视频流中的帧
while True:
# 从线程视频流中抓取帧并调整其大小
# 最大宽度为 400 像素
frame = vs.read()
frame = imutils.resize(frame, width=400)
# 获取帧尺寸并将其转换为 blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
0.007843, (300, 300), 127.5)
# 通过网络传递blob并获得检测
# 预测
net.setInput(blob)
detections = net.forward()
# 循环检测
for i in np.arange(0, detections.shape[2]):
# 提取与相关联的置信度（即概率）用来预测
confidence = detections[0, 0, i, 2]
# 通过确保置信度大于最小置信度来过滤无效检测
if confidence > args["confidence"]:
# 从类标签 detections中提取索引，然后计算物体的边界框 (x, y) 坐标
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# 在当前帧上绘制预测
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# 显示输出帧
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# 如果按下 `q` 键，则跳出循环
if key == ord("q"):
break
# 更新 FPS 计数器
fps.update()
# 停止定时器并显示 FPS 信息
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# 做一些清理
cv2.destroyAllWindows()
vs.stop()
- 调用方法:
# 用法
python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
- 测试效果：
3.配置文件
设置卷积层及其模型相关配置
- MobileNetSSD_deploy.prototxt.txt
name: "MobileNet-SSD"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 300
dim: 300
}
layer {
name: "conv0"
type: "Convolution"
bottom: "data"
top: "conv0"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv0/relu"
type: "ReLU"
bottom: "conv0"
top: "conv0"
}
layer {
name: "conv1/dw"
type: "Convolution"
bottom: "conv0"
top: "conv1/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
group: 32
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv1/dw/relu"
type: "ReLU"
bottom: "conv1/dw"
top: "conv1/dw"
}
layer {
name: "conv1"
type: "Convolution"
bottom: "conv1/dw"
top: "conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv1/relu"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "conv2/dw"
type: "Convolution"
bottom: "conv1"
top: "conv2/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
stride: 2
group: 64
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv2/dw/relu"
type: "ReLU"
bottom: "conv2/dw"
top: "conv2/dw"
}
layer {
name: "conv2"
type: "Convolution"
bottom: "conv2/dw"
top: "conv2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv2/relu"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "conv3/dw"
type: "Convolution"
bottom: "conv2"
top: "conv3/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
group: 128
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv3/dw/relu"
type: "ReLU"
bottom: "conv3/dw"
top: "conv3/dw"
}
layer {
name: "conv3"
type: "Convolution"
bottom: "conv3/dw"
top: "conv3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv3/relu"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4/dw"
type: "Convolution"
bottom: "conv3"
top: "conv4/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 2
group: 128
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv4/dw/relu"
type: "ReLU"
bottom: "conv4/dw"
top: "conv4/dw"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv4/dw"
top: "conv4"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv4/relu"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5/dw"
type: "Convolution"
bottom: "conv4"
top: "conv5/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 256
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv5/dw/relu"
type: "ReLU"
bottom: "conv5/dw"
top: "conv5/dw"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv5/dw"
top: "conv5"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv5/relu"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "conv6/dw"
type: "Convolution"
bottom: "conv5"
top: "conv6/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 2
group: 256
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6/dw/relu"
type: "ReLU"
bottom: "conv6/dw"
top: "conv6/dw"
}
layer {
name: "conv6"
type: "Convolution"
bottom: "conv6/dw"
top: "conv6"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6/relu"
type: "ReLU"
bottom: "conv6"
top: "conv6"
}
layer {
name: "conv7/dw"
type: "Convolution"
bottom: "conv6"
top: "conv7/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv7/dw/relu"
type: "ReLU"
bottom: "conv7/dw"
top: "conv7/dw"
}
layer {
name: "conv7"
type: "Convolution"
bottom: "conv7/dw"
top: "conv7"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv7/relu"
type: "ReLU"
bottom: "conv7"
top: "conv7"
}
layer {
name: "conv8/dw"
type: "Convolution"
bottom: "conv7"
top: "conv8/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv8/dw/relu"
type: "ReLU"
bottom: "conv8/dw"
top: "conv8/dw"
}
layer {
name: "conv8"
type: "Convolution"
bottom: "conv8/dw"
top: "conv8"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv8/relu"
type: "ReLU"
bottom: "conv8"
top: "conv8"
}
layer {
name: "conv9/dw"
type: "Convolution"
bottom: "conv8"
top: "conv9/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv9/dw/relu"
type: "ReLU"
bottom: "conv9/dw"
top: "conv9/dw"
}
layer {
name: "conv9"
type: "Convolution"
bottom: "conv9/dw"
top: "conv9"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv9/relu"
type: "ReLU"
bottom: "conv9"
top: "conv9"
}
layer {
name: "conv10/dw"
type: "Convolution"
bottom: "conv9"
top: "conv10/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv10/dw/relu"
type: "ReLU"
bottom: "conv10/dw"
top: "conv10/dw"
}
layer {
name: "conv10"
type: "Convolution"
bottom: "conv10/dw"
top: "conv10"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv10/relu"
type: "ReLU"
bottom: "conv10"
top: "conv10"
}
layer {
name: "conv11/dw"
type: "Convolution"
bottom: "conv10"
top: "conv11/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
group: 512
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv11/dw/relu"
type: "ReLU"
bottom: "conv11/dw"
top: "conv11/dw"
}
layer {
name: "conv11"
type: "Convolution"
bottom: "conv11/dw"
top: "conv11"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv11/relu"
type: "ReLU"
bottom: "conv11"
top: "conv11"
}
layer {
name: "conv12/dw"
type: "Convolution"
bottom: "conv11"
top: "conv12/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 2
group: 512
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv12/dw/relu"
type: "ReLU"
bottom: "conv12/dw"
top: "conv12/dw"
}
layer {
name: "conv12"
type: "Convolution"
bottom: "conv12/dw"
top: "conv12"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 1024
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv12/relu"
type: "ReLU"
bottom: "conv12"
top: "conv12"
}
layer {
name: "conv13/dw"
type: "Convolution"
bottom: "conv12"
top: "conv13/dw"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 1024
pad: 1
kernel_size: 3
group: 1024
engine: CAFFE
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv13/dw/relu"
type: "ReLU"
bottom: "conv13/dw"
top: "conv13/dw"
}
layer {
name: "conv13"
type: "Convolution"
bottom: "conv13/dw"
top: "conv13"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 1024
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv13/relu"
type: "ReLU"
bottom: "conv13"
top: "conv13"
}
layer {
name: "conv14_1"
type: "Convolution"
bottom: "conv13"
top: "conv14_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv14_1/relu"
type: "ReLU"
bottom: "conv14_1"
top: "conv14_1"
}
layer {
name: "conv14_2"
type: "Convolution"
bottom: "conv14_1"
top: "conv14_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv14_2/relu"
type: "ReLU"
bottom: "conv14_2"
top: "conv14_2"
}
layer {
name: "conv15_1"
type: "Convolution"
bottom: "conv14_2"
top: "conv15_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv15_1/relu"
type: "ReLU"
bottom: "conv15_1"
top: "conv15_1"
}
layer {
name: "conv15_2"
type: "Convolution"
bottom: "conv15_1"
top: "conv15_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv15_2/relu"
type: "ReLU"
bottom: "conv15_2"
top: "conv15_2"
}
layer {
name: "conv16_1"
type: "Convolution"
bottom: "conv15_2"
top: "conv16_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv16_1/relu"
type: "ReLU"
bottom: "conv16_1"
top: "conv16_1"
}
layer {
name: "conv16_2"
type: "Convolution"
bottom: "conv16_1"
top: "conv16_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv16_2/relu"
type: "ReLU"
bottom: "conv16_2"
top: "conv16_2"
}
layer {
name: "conv17_1"
type: "Convolution"
bottom: "conv16_2"
top: "conv17_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv17_1/relu"
type: "ReLU"
bottom: "conv17_1"
top: "conv17_1"
}
layer {
name: "conv17_2"
type: "Convolution"
bottom: "conv17_1"
top: "conv17_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv17_2/relu"
type: "ReLU"
bottom: "conv17_2"
top: "conv17_2"
}
layer {
name: "conv11_mbox_loc"
type: "Convolution"
bottom: "conv11"
top: "conv11_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 12
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv11_mbox_loc_perm"
type: "Permute"
bottom: "conv11_mbox_loc"
top: "conv11_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv11_mbox_loc_flat"
type: "Flatten"
bottom: "conv11_mbox_loc_perm"
top: "conv11_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv11_mbox_conf"
type: "Convolution"
bottom: "conv11"
top: "conv11_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 63
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv11_mbox_conf_perm"
type: "Permute"
bottom: "conv11_mbox_conf"
top: "conv11_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv11_mbox_conf_flat"
type: "Flatten"
bottom: "conv11_mbox_conf_perm"
top: "conv11_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv11_mbox_priorbox"
type: "PriorBox"
bottom: "conv11"
bottom: "data"
top: "conv11_mbox_priorbox"
prior_box_param {
min_size: 60.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
offset: 0.5
}
}
layer {
name: "conv13_mbox_loc"
type: "Convolution"
bottom: "conv13"
top: "conv13_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv13_mbox_loc_perm"
type: "Permute"
bottom: "conv13_mbox_loc"
top: "conv13_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv13_mbox_loc_flat"
type: "Flatten"
bottom: "conv13_mbox_loc_perm"
top: "conv13_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv13_mbox_conf"
type: "Convolution"
bottom: "conv13"
top: "conv13_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 126
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv13_mbox_conf_perm"
type: "Permute"
bottom: "conv13_mbox_conf"
top: "conv13_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv13_mbox_conf_flat"
type: "Flatten"
bottom: "conv13_mbox_conf_perm"
top: "conv13_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv13_mbox_priorbox"
type: "PriorBox"
bottom: "conv13"
bottom: "data"
top: "conv13_mbox_priorbox"
prior_box_param {
min_size: 105.0
max_size: 150.0
aspect_ratio: 2.0
aspect_ratio: 3.0
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
offset: 0.5
}
}
layer {
name: "conv14_2_mbox_loc"
type: "Convolution"
bottom: "conv14_2"
top: "conv14_2_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv14_2_mbox_loc_perm"
type: "Permute"
bottom: "conv14_2_mbox_loc"
top: "conv14_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv14_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv14_2_mbox_loc_perm"
top: "conv14_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv14_2_mbox_conf"
type: "Convolution"
bottom: "conv14_2"
top: "conv14_2_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 126
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv14_2_mbox_conf_perm"
type: "Permute"
bottom: "conv14_2_mbox_conf"
top: "conv14_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv14_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv14_2_mbox_conf_perm"
top: "conv14_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv14_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv14_2"
bottom: "data"
top: "conv14_2_mbox_priorbox"
prior_box_param {
min_size: 150.0
max_size: 195.0
aspect_ratio: 2.0
aspect_ratio: 3.0
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
offset: 0.5
}
}
layer {
name: "conv15_2_mbox_loc"
type: "Convolution"
bottom: "conv15_2"
top: "conv15_2_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv15_2_mbox_loc_perm"
type: "Permute"
bottom: "conv15_2_mbox_loc"
top: "conv15_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv15_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv15_2_mbox_loc_perm"
top: "conv15_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv15_2_mbox_conf"
type: "Convolution"
bottom: "conv15_2"
top: "conv15_2_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 126
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv15_2_mbox_conf_perm"
type: "Permute"
bottom: "conv15_2_mbox_conf"
top: "conv15_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv15_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv15_2_mbox_conf_perm"
top: "conv15_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv15_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv15_2"
bottom: "data"
top: "conv15_2_mbox_priorbox"
prior_box_param {
min_size: 195.0
max_size: 240.0
aspect_ratio: 2.0
aspect_ratio: 3.0
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
offset: 0.5
}
}
layer {
name: "conv16_2_mbox_loc"
type: "Convolution"
bottom: "conv16_2"
top: "conv16_2_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv16_2_mbox_loc_perm"
type: "Permute"
bottom: "conv16_2_mbox_loc"
top: "conv16_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv16_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv16_2_mbox_loc_perm"
top: "conv16_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv16_2_mbox_conf"
type: "Convolution"
bottom: "conv16_2"
top: "conv16_2_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 126
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv16_2_mbox_conf_perm"
type: "Permute"
bottom: "conv16_2_mbox_conf"
top: "conv16_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv16_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv16_2_mbox_conf_perm"
top: "conv16_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv16_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv16_2"
bottom: "data"
top: "conv16_2_mbox_priorbox"
prior_box_param {
min_size: 240.0
max_size: 285.0
aspect_ratio: 2.0
aspect_ratio: 3.0
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
offset: 0.5
}
}
layer {
name: "conv17_2_mbox_loc"
type: "Convolution"
bottom: "conv17_2"
top: "conv17_2_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv17_2_mbox_loc_perm"
type: "Permute"
bottom: "conv17_2_mbox_loc"
top: "conv17_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv17_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv17_2_mbox_loc_perm"
top: "conv17_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv17_2_mbox_conf"
type: "Convolution"
bottom: "conv17_2"
top: "conv17_2_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 126
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv17_2_mbox_conf_perm"
type: "Permute"
bottom: "conv17_2_mbox_conf"
top: "conv17_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv17_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv17_2_mbox_conf_perm"
top: "conv17_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv17_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv17_2"
bottom: "data"
top: "conv17_2_mbox_priorbox"
prior_box_param {
min_size: 285.0
max_size: 300.0
aspect_ratio: 2.0
aspect_ratio: 3.0
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
offset: 0.5
}
}
layer {
name: "mbox_loc"
type: "Concat"
bottom: "conv11_mbox_loc_flat"
bottom: "conv13_mbox_loc_flat"
bottom: "conv14_2_mbox_loc_flat"
bottom: "conv15_2_mbox_loc_flat"
bottom: "conv16_2_mbox_loc_flat"
bottom: "conv17_2_mbox_loc_flat"
top: "mbox_loc"
concat_param {
axis: 1
}
}
layer {
name: "mbox_conf"
type: "Concat"
bottom: "conv11_mbox_conf_flat"
bottom: "conv13_mbox_conf_flat"
bottom: "conv14_2_mbox_conf_flat"
bottom: "conv15_2_mbox_conf_flat"
bottom: "conv16_2_mbox_conf_flat"
bottom: "conv17_2_mbox_conf_flat"
top: "mbox_conf"
concat_param {
axis: 1
}
}
layer {
name: "mbox_priorbox"
type: "Concat"
bottom: "conv11_mbox_priorbox"
bottom: "conv13_mbox_priorbox"
bottom: "conv14_2_mbox_priorbox"
bottom: "conv15_2_mbox_priorbox"
bottom: "conv16_2_mbox_priorbox"
bottom: "conv17_2_mbox_priorbox"
top: "mbox_priorbox"
concat_param {
axis: 2
}
}
layer {
name: "mbox_conf_reshape"
type: "Reshape"
bottom: "mbox_conf"
top: "mbox_conf_reshape"
reshape_param {
shape {
dim: 0
dim: -1
dim: 21
}
}
}
layer {
name: "mbox_conf_softmax"
type: "Softmax"
bottom: "mbox_conf_reshape"
top: "mbox_conf_softmax"
softmax_param {
axis: 2
}
}
layer {
name: "mbox_conf_flatten"
type: "Flatten"
bottom: "mbox_conf_softmax"
top: "mbox_conf_flatten"
flatten_param {
axis: 1
}
}
layer {
name: "detection_out"
type: "DetectionOutput"
bottom: "mbox_loc"
bottom: "mbox_conf_flatten"
bottom: "mbox_priorbox"
top: "detection_out"
include {
phase: TEST
}
detection_output_param {
num_classes: 21
share_location: true
background_label_id: 0
nms_param {
nms_threshold: 0.45
top_k: 100
}
code_type: CENTER_SIZE
keep_top_k: 100
confidence_threshold: 0.25
}
}
4.模型下载
MobileNetSSD_deploy.caffemodel: https://share.weiyun.com/Wi04sqO7
[object_detect]使用MobileNetSSD进行对象检测的更多相关文章
- [OpenCV-Python] OpenCV 中计算摄影学 部分 IX 对象检测 部分 X
部分 IX计算摄影学 OpenCV-Python 中文教程(搬运)目录 49 图像去噪目标 • 学习使用非局部平均值去噪算法去除图像中的噪音 • 学习函数 cv2.fastNlMeansDenoisi ...
- 浏览器。浏览器对象检测、Chrome调试工具
chrome浏览器的flash问题: 2017-12-26 chrome浏览器的flash有无法显示无法正常运行的问题时,解决方法如下: https://qzonestyle.gtimg.cn/qzo ...
- 斯坦福新深度学习系统 NoScope：视频对象检测快1000倍
以作备份,来源http://jiasuhui.com/archives/178954 本文由“新智元”(微信ID:AI_era)编译,来源:dawn.cs.stanford.edu,编译:刘小芹 斯坦 ...
- 计算机视觉中的对象检测，Python用几段代码就能实现
目前计算机视觉(CV)与自然语言处理(NLP)及语音识别并列为人工智能三大热点方向,而计算机视觉中的对象检测(objectdetection)应用非常广泛,比如自动驾驶.视频监控.工业质检.医疗诊断等 ...
- 【YOLO】实时对象检测使用体验
官网:https://pjreddie.com/darknet/yolo/ 以下全部在服务器上完成,服务器上是有opencv等. 1.安装Darknet git clone https://githu ...
- 常用 对象检测 api
isPrototypeOf() 判断某个 proptotype 对象和某个实例之间的关系 alert(Cat.prototype.isPrototypeOf(cat1)); //true ale ...
- python imageai 对象检测、对象识别
imageai库里面提供了目标识别,其实也可以说是目标检测,和现在很多的收集一样就是物体识别.他可以帮你识别出各种各样生活中遇见的事物.比如猫.狗.车.马.人.电脑.收集等等. 感觉imageai有点 ...
- 针对unicode对象---检测字符串是否只由数字组成
- OpenCVSSDpython目标探测对象检测
1.请参考大牛博客链接 https://www.aiuai.cn/aifarm822.html
随机推荐
- A Child's History of England.39
He had become Chancellor, when the King thought of making him Archbishop. He was clever, gay, well e ...
- 内存中 1k 代表什么
1K也就是 1KB == 1000 bytes == 1000 *8 位 通常一个地址里面有8位,就是说一个房间里面能存8个0或者1
- JAXB—Java类与XML文件之间转换
JAXB-Java类与XML文件之间转换 简介 JAXB(Java Architecture for XML Binding) 是一个业界的标准,是一项可以根据XML Schema产生 ...
- JmxTest
package mbeanTest; import java.util.Set; import javax.management.Attribute; import javax.management. ...
- 【JAVA】【Basic】MacOS上搭建JAVA开发环境
1. JRE 1.1. 下载地址:https://www.oracle.com/technetwork/java/javase/downloads/index.html 1.1.1. dmg格式安装: ...
- JDBC(2)：JDBC对数据库进行CRUD
一. statement对象 JDBC程序中的Connection用于代表数据库的链接:Statement对象用于向数据库发送SQL语句:ResultSet用于代表Sql语句的执行结果 JDBC中的s ...
- 【编程思想】【设计模式】【行为模式Behavioral】chaining_method
Python版 https://github.com/faif/python-patterns/blob/master/behavioral/chaining_method.py #!/usr/bin ...
- 代码仓库gogs的基本配置使用
目录 一.基本功能介绍 主板说明 页面说明 用户设置 二.仓库 新建仓库 迁移仓库 仓库介绍 三.组织和团队 创建新组织 创建团队 一.基本功能介绍 主板说明 图中1表示自己个人账户下的仓库(所有权属 ...
- yum的epel源
目录 一.centos7的源 一.centos7的源 以下一个不行,可以试试另一个 rpm -ivh https://mirrors.aliyun.com/epel/epel-release-late ...
- 腾讯新闻基于 Flink PipeLine 模式的实践
摘要 :随着社会消费模式以及经济形态的发展变化,将催生新的商业模式.腾讯新闻作为一款集游戏.教育.电商等一体的新闻资讯平台.服务亿万用户,业务应用多.数据量大.加之业务增长.场景更加复杂,业务对实时 ...