Fast RCNN 训练自己的数据集（3训练和检测）
Fast RCNN 训练自己的数据集（3训练和检测）
在之前两篇文章中我介绍了怎么编译Fast RCNN，和怎么修改Fast RCNN的读取数据接口，接下来我来说明一下怎么来训练网络和之后的检测过程
先给看一下极好的检测效果
1.预训练模型介绍
首先在data目录下，有两个目录就是之前在1中解压好
- fast_rcnn_models/
- imagenet_models/
fast_rcnn_model文件夹下面是作者用fast rcnn训练好的三个网络,分别对应着小、中、大型网络，大家可以试用一下这几个网络，看一些检测效果，他们训练都迭代了40000次，数据集都是pascal_voc的数据集。
- caffenet_fast_rcnn_iter_40000.caffemodel
- vgg_cnn_m_1024_fast_rcnn_iter_40000.caffemodel
- vgg16_fast_rcnn_iter_40000.caffemodel
imagenet_model文件夹下面是在Imagenet上训练好的通用模型，在这里用来初始化网络的参数
- CaffeNet.v2.caffemodel
- VGG_CNN_M_1024.v2.caffemodel
- VGG16.v2.caffemodel
在这里我比较推荐先用中型网络训练，中型网络训练和检测的速度都比较快，效果也都比较理想，大型网络的话训练速度比较慢，我当时是5000多个标注信息，网络配置默认，中型网络训练大概两三个小时，大型网络的话用十几个小时，需要注意的是网络训练最好用GPU，CPU的话太慢了，我当时用的实验室的服务器，有16块Tesla K80，用起来真的是灰常爽！
2. 修改模型文件配置
模型文件在models下面对应的网络文件夹下，在这里我用中型网络的配置文件修改为例子
因此，首先打开网络的模型文件夹，打开train.prototxt
修改的地方重要有三个
分别是个地方
- 首先在data层把num_classes 从原来的21类 20类+背景 ，改成 2类 车+背景
- 接在在cls_score层把num_output 从原来的21 改成 2
- 在bbox_pred层把num_output 从原来的84 改成8， 为检测类别个数乘以4，比如这里是2类那就是2*4=8
OK，如果你要进一步修改网络训练中的学习速率，步长，gamma值，以及输出模型的名字，需要在同目录下的solver.prototxt中修改。如下图：
train_net: "models/VGG_CNN_M_1024/train.prototxt"
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize: 30000
display: 20
average_loss: 100
momentum: 0.9
weight_decay: 0.0005
# We disable standard caffe solver snapshotting and implement our own snapshot
# function snapshot: 0 # We still use the snapshot prefix, though snapshot_prefix: "vgg_cnn_m_1024_fast_rcnn" #debug_info: true 3.启动Fast RCNN网络训练
启动训练：
./tools/train_net.py --gpu 11 --solver models/VGG_CNN_M_1024_LOUYIHANG/solver.prototxt --weights data/imagenet_models/VGG_CNN_M_1024.v2.caffemodel --imdb KakouTrain
参数讲解：
- 这里的--是两个-，markdown写的，大家不要输错
- train_net.py是网络的训练文件，之后的参数都是附带的输入参数
- --gpu 代表机器上的GPU编号，如果是nvidia系列的tesla显卡，可以在终端中输入nvidia-smi来查看当前的显卡负荷，选择合适的显卡
- --solver 代表模型的配置文件，train.prototxt的文件路径已经包含在这个文件之中
- --weights 代表初始化的权重文件，这里用的是Imagenet上预训练好的模型，中型的网络我们选择用VGG_CNN_M_1024.v2.caffemodel
- --imdb 这里给出的训练的数据库名字需要在factory.py的__sets中，我在文件里面有__sets['KakouTrain']，train_net.py这个文件会调用factory.py再生成kakou这个类，来读取数据
4.启动Fast RCNN网络检测
我修改了tools下面的demo.py这个文件，用来做检测，并且将检测的坐标结果输出到相应的txt文件中可以看到原始的demo.py 是用网络测试了两张图像，并做可视化输出，有具体的检测效果，但是我是在Linux服务器的终端下，没有display device，因此部分代码要少做修改
下面是原始的demo.py：
#!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
""" Demo script showing detections in sample images. See README.md for installation instructions before running. """
import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from utils.cython_nms import nms
from utils.timer import Timer
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse
CLASSES = ('__background__',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
NETS = {'vgg16': ('VGG16',
'vgg16_fast_rcnn_iter_40000.caffemodel'),
'vgg_cnn_m_1024': ('VGG_CNN_M_1024',
'vgg_cnn_m_1024_fast_rcnn_iter_40000.caffemodel'),
'caffenet': ('CaffeNet',
'caffenet_fast_rcnn_iter_40000.caffemodel')}
def vis_detections(im, class_name, dets, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,
thresh),
fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.draw()
def demo(net, image_name, classes):
"""Detect object classes in an image using pre-computed object proposals."""
# Load pre-computed Selected Search object proposals
box_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo',
image_name + '_boxes.mat')
obj_proposals = sio.loadmat(box_file)['boxes']
# Load the demo image
im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', image_name + '.jpg')
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, obj_proposals)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls in classes:
cls_ind = CLASSES.index(cls)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,
CONF_THRESH)
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
default=0, type=int)
parser.add_argument('--cpu', dest='cpu_mode',
help='Use CPU mode (overrides --gpu)',
action='store_true')
parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
choices=NETS.keys(), default='vgg16')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
prototxt = os.path.join(cfg.ROOT_DIR, 'models', NETS[args.demo_net][0],
'test.prototxt')
caffemodel = os.path.join(cfg.ROOT_DIR, 'data', 'fast_rcnn_models',
NETS[args.demo_net][1])
if not os.path.isfile(caffemodel):
raise IOError(('{:s} not found.\nDid you run ./data/script/'
'fetch_fast_rcnn_models.sh?').format(caffemodel))
if args.cpu_mode:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
print '\n\nLoaded network {:s}'.format(caffemodel)
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/000004.jpg'
demo(net, '000004', ('car',))
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/001551.jpg'
demo(net, '001551', ('sofa', 'tvmonitor'))
plt.show()
复制这个demo.py 修改成CarFaceTest.py，下面是修改后的文件修改后的文件主要是添加了outputDetectionResult和runDetection两个函数， 添加了部分注释
#!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
""" Demo script showing detections in sample images. See README.md for installation instructions before running. """
import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from utils.cython_nms import nms
from utils.timer import Timer
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse
#CLASSES = ('__background__','aeroplane','bicycle','bird','boat',
# 'bottle','bus','car','cat','chair','cow','diningtable','dog','horse'
# 'motorbike','person','pottedplant','sheep','sofa','train','tvmonitor')
CLASSES = ('__background__','car') #需要跟自己训练的数据集中的类别一致，原来是21类的voc数据集，自己的数据集就是car和background
NETS = {'vgg16': ('VGG16',
'vgg16_fast_rcnn_iter_40000.caffemodel'),
'vgg_cnn_m_1024': ('VGG_CNN_M_1024',
'vgg_cnn_m_1024_fast_rcnn_iter_40000.caffemodel'),
'vgg_cnn_m_1024_louyihang': ('VGG_CNN_M_1024_LOUYIHANG',
'vgg_cnn_m_1024_fast_rcnn_louyihang_iter_40000.caffemodel'),
'caffenet': ('CaffeNet',
'caffenet_fast_rcnn_iter_40000.caffemodel'),
'caffenet_louyihang':('CaffeNet_LOUYIHANG',
'caffenet_fast_rcnn_louyihang_iter_40000.caffemodel'),
'vgg16_louyihang':('VGG16_LOUYIHANG',
'vgg16_fast_rcnn_louyihang_iter_40000.caffemodel')}#映射到对应的模型文件
def outputDetectionResult(im, class_name, dets, thresh=0.5): #打开相应的输出文件
outputFile = open('CarDetectionResult.txt')
inds = np.where(dets[:,-1] >= thresh)[0]
if len(inds) == 0:
return
def runDetection (net, basePath, testFileName,classes):#这个函数是自己后加的，取代了demo函数，给定测试数据列表
ftest = open(testFileName,'r')
imageFileName = basePath+'/' + ftest.readline().strip()
num = 1
outputFile = open('CarDetectionResult.txt','w')
while imageFileName:
print imageFileName
print 'now is ', num
num +=1
imageFileBaseName = os.path.basename(imageFileName)
imageFileDir = os.path.dirname(imageFileName)
boxFileName = imageFileDir +'/'+imageFileBaseName.replace('.jpg','_boxes.mat')
print boxFileName
obj_proposals = sio.loadmat(boxFileName)['boxes']
#obj_proposals[:,2] = obj_proposals[:, 2] + obj_proposals[:, 0]#这里也需要注意，OP里面的坐标数据是否为x1y1x2y2还是x1y1wh
#obj_proposals[:,3] = obj_proposals[:, 3] + obj_proposals[:, 1]
im = cv2.imread(imageFileName)
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, obj_proposals)#检测函数
timer.toc()
print ('Detection took {:.3f} for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
CONF_THRESH = 0.8
NMS_THRESH = 0.3#NMS参数用来控制非极大值抑制
for cls in classes:
cls_ind = CLASSES.index(cls)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,
CONF_THRESH)
inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
print 'inds.size', inds.size
if len(inds) != 0:
outputFile.write(imageFileName+' ')
outputFile.write(str(inds.size)+' ')将检测的结果写出相应的文件里
for i in inds:
bbox = dets[i, :4]
outputFile.write(str(int(bbox[0]))+' '+ str(int(bbox[1]))+' '+ str(int(bbox[2]))+' '+ str(int(bbox[3]))+' ')
outputFile.write('\n')
else:
outputFile.write(imageFileName +' 0' '\n')
temp = ftest.readline().strip()
if temp:
imageFileName = basePath+'/' + temp
else:
break
def vis_detections(im, class_name, dets, thresh=0.5):#这个函数需要加以说明，这个函数虽然没有用，但是我的服务器上没有输出设备
"""Draw detected bounding boxes."""#因此要将部分用到显示的函数给注释掉，否则运行会报错
inds = np.where(dets[:, -1] >= thresh)[0]
print 'inds.shape', inds.shape
print inds
print 'inds.size', inds.size
if len(inds) == 0:
return
#im = im[:, :, (2, 1, 0)]
#fig, ax = plt.subplots(figsize=(12, 12))
#ax.imshow(im, aspect='equal')
#for i in inds:
# bbox = dets[i, :4]
# score = dets[i, -1]
# ax.add_patch(
# plt.Rectangle((bbox[0], bbox[1]),
# bbox[2] - bbox[0],
# bbox[3] - bbox[1], fill=False,
# edgecolor='red', linewidth=3.5)
# )
# ax.text(bbox[0], bbox[1] - 2,
# '{:s} {:.3f}'.format(class_name, score),
# bbox=dict(facecolor='blue', alpha=0.5),
# fontsize=14, color='white')
#ax.set_title(('{} detections with '
# 'p({} | box) >= {:.1f}').format(class_name, class_name,
# thresh),
# fontsize=14)
#plt.axis('off')
#plt.tight_layout()
#plt.draw()
def demo(net, image_name, classes):#原来的demo函数，没有修改
"""Detect object classes in an image using pre-computed object proposals."""
# Load pre-computed Selected Search object proposals
#box_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo',image_name + '_boxes.mat')
basePath='/home/chenjie/DataSet/500CarTestDataSet2'
box_file = os.path.join(basePath,image_name + '_boxes.mat')
obj_proposals = sio.loadmat(box_file)['boxes']
# Load the demo image
#im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', image_name + '.jpg')
im_file = os.path.join(basePath, image_name + '.jpg')
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, obj_proposals)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls in classes:
cls_ind = CLASSES.index(cls)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,
CONF_THRESH)
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
default=0, type=int)
parser.add_argument('--cpu', dest='cpu_mode',
help='Use CPU mode (overrides --gpu)',
action='store_true')
parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
choices=NETS.keys(), default='vgg16')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
prototxt = os.path.join(cfg.ROOT_DIR, 'models', NETS[args.demo_net][0],
'test.prototxt')
#caffemodel = os.path.join(cfg.ROOT_DIR, 'data', 'fast_rcnn_models',
# NETS[args.demo_net][1])
#caffemodel = '/home/chenjie/fast-rcnn/output/default/KakouTrain/vgg16_fast_rcnn_louyihang_iter_40000.caffemodel'
#caffemodel = '/home/chenjie/louyihang/fast-rcnn/output/default/KakouTrain/caffenet_fast_rcnn_louyihang_iter_40000.caffemodel'
caffemodel = '/home/chenjie/fast-rcnn/output/default/KakouTrain/vgg_cnn_m_1024_fast_rcnn_louyihang_iter_40000.caffemodel'#我在这里直接指定了训练好的模型文件，训练好的模型文件是在工程根目录下的，output/default/对应的数据库名字下面
if not os.path.isfile(caffemodel):
raise IOError(('{:s} not found.\nDid you run ./data/script/'
'fetch_fast_rcnn_models.sh?').format(caffemodel))
if args.cpu_mode:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
print '\n\nLoaded network {:s}'.format(caffemodel)
#demo(net, 'Target0/000001', ('car',))
#输入对应的测试图像列表，需要在同级目录下摆放同名的_boxes.mat文件，它会自动的替换后缀名！
#runDetection(net, '/home/chenjie/DataSet/temptest','/home/chenjie/DataSet/temptest/Imagelist.txt',('car',))
runDetection(net, '/home/chenjie/DataSet/500CarTestDataSet2','/home/chenjie/DataSet/500CarTestDataSet2/Imagelist.txt',('car',))
#runDetection(net, '/home/chenjie/DataSet/Kakou_Test_Scale0.25/','/home/chenjie/DataSet/Kakou_Test_Scale0.25/imagelist.txt',('car',))
#runDetection(net, '/home/chenjie/DataSet/Images_Version1_Test_Boxes','/home/chenjie/DataSet/Images_Version1_Test_Boxes/ImageList_Version1_List.txt',('car',))
#plt.show()
5.检测结果
训练数据集
首先给出我的训练数据集，其实我的训练数据集并不是太复杂的
测试数据集
输出检测结果到txt文件中，
测试效果
**在复杂场景下的测试效果非常好，速度也非常快，中型网络监测平均每张在K80显卡下时0.1~0.2S左右，图像的尺寸是480*640，6000张测试数据集下达到的准确率是98%!!!**
