目标检测SSD+Tensorflow 训练自己的数据集

1.代码地址：https://github.com/balancap/SSD-Tensorflow，下载该代码到本地

2.解压ssd_300_vgg.ckpt.zip 到checkpoint文件夹下




3.测试一下看看，在notebooks中创建demo_test.py,其实就是复制ssd_notebook.ipynb中的代码，该py文件是完成对于单张图片的测试，对Jupyter不熟，就自己改了，感觉这样要方便一些。


import os import math import random import numpy as np import tensorflow as tf
import cv2 slim = tf.contrib.slim import matplotlib.pyplot as plt import
matplotlib.image as mpimg import sys sys.path.append('../') from nets import
ssd_vgg_300, ssd_common, np_methods from preprocessing import
ssd_vgg_preprocessing from notebooks import visualization # TensorFlow session:
grow memory when needed. TF, DO NOT USE ALL MY GPU MEMORY!!! gpu_options =
tf.GPUOptions(allow_growth=True) config =
tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options) isess =
tf.InteractiveSession(config=config) # Input placeholder. net_shape = (300,
300) data_format = 'NHWC' img_input = tf.placeholder(tf.uint8, shape=(None,
None, 3)) # Evaluation pre-processing: resize to SSD net shape. image_pre,
labels_pre, bboxes_pre, bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(
img_input, None, None, net_shape, data_format,
resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE) image_4d =
tf.expand_dims(image_pre, 0) # Define the SSD model. reuse = True if 'ssd_net'
in locals() else None ssd_net = ssd_vgg_300.SSDNet() with
slim.arg_scope(ssd_net.arg_scope(data_format=data_format)): predictions,
localisations, _, _ = ssd_net.net(image_4d, is_training=False, reuse=reuse) #
Restore SSD model. ckpt_filename = '../checkpoints/ssd_300_vgg.ckpt' #
ckpt_filename = '../checkpoints/VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt'
isess.run(tf.global_variables_initializer()) saver = tf.train.Saver()
saver.restore(isess, ckpt_filename) # SSD default anchor boxes. ssd_anchors =
ssd_net.anchors(net_shape) # Main image processing routine. def
process_image(img, select_threshold=0.5, nms_threshold=.45, net_shape=(300,
300)): # Run SSD network. rimg, rpredictions, rlocalisations, rbbox_img =
isess.run([image_4d, predictions, localisations, bbox_img],
feed_dict={img_input: img}) # Get classes and bboxes from the net outputs.
rclasses, rscores, rbboxes = np_methods.ssd_bboxes_select( rpredictions,
rlocalisations, ssd_anchors, select_threshold=select_threshold,
img_shape=net_shape, num_classes=21, decode=True) rbboxes =
np_methods.bboxes_clip(rbbox_img, rbboxes) rclasses, rscores, rbboxes =
np_methods.bboxes_sort(rclasses, rscores, rbboxes, top_k=400) rclasses,
rscores, rbboxes = np_methods.bboxes_nms(rclasses, rscores, rbboxes,
nms_threshold=nms_threshold) # Resize bboxes to original image shape. Note:
useless for Resize.WARP! rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes)
return rclasses, rscores, rbboxes # Test on some demo image and visualize
output. #测试的文件夹 path = '../demo/' image_names = sorted(os.listdir(path))
#文件夹中的第几张图，-1代表最后一张 img = mpimg.imread(path + image_names[-1]) rclasses,
rscores, rbboxes = process_image(img) # visualization.bboxes_draw_on_img(img,
rclasses, rscores, rbboxes, visualization.colors_plasma)
visualization.plt_bboxes(img, rclasses, rscores, rbboxes)
 


4.将自己的数据集做成VOC2007格式放在该工程下面


5. 修改datasets文件夹中pascalvoc_common.py文件，将训练类修改别成自己的
#原始的 # VOC_LABELS = { # 'none': (0, 'Background'), # 'aeroplane': (1,
'Vehicle'), # 'bicycle': (2, 'Vehicle'), # 'bird': (3, 'Animal'), # 'boat': (4,
'Vehicle'), # 'bottle': (5, 'Indoor'), # 'bus': (6, 'Vehicle'), # 'car': (7,
'Vehicle'), # 'cat': (8, 'Animal'), # 'chair': (9, 'Indoor'), # 'cow': (10,
'Animal'), # 'diningtable': (11, 'Indoor'), # 'dog': (12, 'Animal'), # 'horse':
(13, 'Animal'), # 'motorbike': (14, 'Vehicle'), # 'person': (15, 'Person'), #
'pottedplant': (16, 'Indoor'), # 'sheep': (17, 'Animal'), # 'sofa': (18,
'Indoor'), # 'train': (19, 'Vehicle'), # 'tvmonitor': (20, 'Indoor'), # } #修改后的
VOC_LABELS = { 'none': (0, 'Background'), 'pantograph':(1,'Vehicle'), }
6. 
将图像数据转换为tfrecods格式，修改datasets文件夹中的pascalvoc_to_tfrecords.py文件，然后更改文件的83行读取方式为’rb‘，如果你的文件不是.jpg格式，也可以修改图片的类型。



此外， 修改67行，可以修改几张图片转为一个tfrecords



7.运行tf_convert_data.py文件，但是需要传给它一些参数：

linux
在SSD-Tensorflow-master文件夹下创建tf_conver_data.sh,文件写入内容如下：

DATASET_DIR=./VOC2007/     #VOC数据保存的文件夹（VOC的目录格式未改变）  
OUTPUT_DIR=./tfrecords_  #自己建立的保存tfrecords数据的文件夹       
python ./tf_convert_data.py \     
  --dataset_name=pascalvoc \         
  --dataset_dir=${DATASET_DIR} \   
  --output_name=voc_2007_train \ 
  --output_dir=${OUTPUT_DIR}  


windows     +pychram

配置pycharm-->run-->Edit Configuration

Script parameters中写入：--dataset_name=pascalvoc --dataset_dir=./VOC2007/
--output_name=voc_2007_train --output_dir=./tfrecords_

运行tf_convert_data.py文件



生成tfrecords文件过程中你会看到 生成tfrecords文件完毕后你会看到

 



8.训练模型train_ssd_network.py文件中修改

 

train_ssd_network.py文件中网络参数配置，若需要改，在此文件中进行修改，如：



其他需要修改的地方

a.   nets/ssd_vgg_300.py  (因为使用此网络结构) ，修改87 和88行的类别
b. train_ssd_network.py,修改类别120行，GPU占用量，学习率，batch_size等
 c eval_ssd_network.py 修改类别，66行
d. datasets/pascalvoc_2007.py 根据自己的训练数据修改整个文件
# (Images, Objects) statistics on every class. # TRAIN_STATISTICS = { #
'none': (0, 0), # 'aeroplane': (238, 306), # 'bicycle': (243, 353), # 'bird':
(330, 486), # 'boat': (181, 290), # 'bottle': (244, 505), # 'bus': (186, 229),
# 'car': (713, 1250), # 'cat': (337, 376), # 'chair': (445, 798), # 'cow':
(141, 259), # 'diningtable': (200, 215), # 'dog': (421, 510), # 'horse': (287,
362), # 'motorbike': (245, 339), # 'person': (2008, 4690), # 'pottedplant':
(245, 514), # 'sheep': (96, 257), # 'sofa': (229, 248), # 'train': (261, 297),
# 'tvmonitor': (256, 324), # 'total': (5011, 12608), # } # TEST_STATISTICS = {
# 'none': (0, 0), # 'aeroplane': (1, 1), # 'bicycle': (1, 1), # 'bird': (1, 1),
# 'boat': (1, 1), # 'bottle': (1, 1), # 'bus': (1, 1), # 'car': (1, 1), #
'cat': (1, 1), # 'chair': (1, 1), # 'cow': (1, 1), # 'diningtable': (1, 1), #
'dog': (1, 1), # 'horse': (1, 1), # 'motorbike': (1, 1), # 'person': (1, 1), #
'pottedplant': (1, 1), # 'sheep': (1, 1), # 'sofa': (1, 1), # 'train': (1, 1),
# 'tvmonitor': (1, 1), # 'total': (20, 20), # } # SPLITS_TO_SIZES = { #
'train': 5011, # 'test': 4952, # } # (Images, Objects) statistics on every
class. TRAIN_STATISTICS = { 'none': (0, 0), 'pantograph': (1000, 1000), }
TEST_STATISTICS = { 'none': (0, 0), 'pantograph': (1000, 1000), }
SPLITS_TO_SIZES = { 'train': 500, 'test': 500, }
9.通过加载预训练好的vgg16模型,训练网络

下载预训练好的vgg16模型，解压放入checkpoint文件中，如果找不到vgg_16.ckpt文件，可以在下面的链接中点击下载。



链接：https://pan.baidu.com/s/1diWbdJdjVbB3AWN99406nA 密码：ge3x

按照之前的方式，同样，如果你是linux用户，你可以新建一个.sh文件，文件里写入
DATASET_DIR=./tfrecords_/ TRAIN_DIR=./train_model/
CHECKPOINT_PATH=./checkpoints/vgg_16.ckpt python3 ./train_ssd_network.py \
--train_dir=./train_model/ \ #训练生成模型的存放路径 --dataset_dir=./tfrecords_/ \ #数据存放路径
--dataset_name=pascalvoc_2007 \ #数据名的前缀 --dataset_split_name=train \
--model_name=ssd_300_vgg \ #加载的模型的名字
--checkpoint_path=./checkpoints/vgg_16.ckpt \ #所加载模型的路径
--checkpoint_model_scope=vgg_16 \ #所加载模型里面的作用域名
--checkpoint_exclude_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box
\
--trainable_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box
\ --save_summaries_secs=60 \ #每60s保存一下日志 --save_interval_secs=600 \
#每600s保存一下模型 --weight_decay=0.0005 \ #正则化的权值衰减的系数 --optimizer=adam \ #选取的最优化函数
--learning_rate=0.001 \ #学习率 --learning_rate_decay_factor=0.94 \ #学习率的衰减因子
--batch_size=24 \ --gpu_memory_fraction=0.9 #指定占用gpu内存的百分比
如果你是windows+pycharm中运行，除了在上述的run中Edit
Configuration配置，你还可以打开Terminal，在这里运行代码，输入即可
python ./train_ssd_network.py --train_dir=./train_model/
--dataset_dir=./tfrecords_/ --dataset_name=pascalvoc_2007
--dataset_split_name=train --model_name=ssd_300_vgg
--checkpoint_path=./checkpoints/vgg_16.ckpt --checkpoint_model_scope=vgg_16
--checkpoint_exclude_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box
--trainable_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box
--save_summaries_secs=60 --save_interval_secs=600 --weight_decay=0.0005
--optimizer=adam --learning_rate=0.001 --learning_rate_decay_factor=0.94
--batch_size=24 --gpu_memory_fraction=0.9
 

 

 

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