YOLOV3目标检测框架搭建和训练自己模型的流程

2023-12-28 08:43:16

YOLOV3目标检测框架搭建和训练自己模型的步骤（超详细）

Ubuntu18.04安装labelImg标注工具—pyqt5 安装步骤

# 不需要进入到虚拟环境中
sudo apt-get install pyqt5-dev-tools 
sudo pip3 install lxml 
git clone https://github.com/tzutalin/labelImg.git 
cd labelImg 
make qt5py3      # 用make all 会导致先识别pyqt4.
python3 labelImg.py #打开labelImg

使用图像标注工具

Open可导入单张图片。
Open Dir可打开文件夹目录，然后可以用Next Image和Prev Image查看所有图片。
Change Save Dir可以更改xml文件保存的路径。
Verify Image可更改xml文件的内容。
Save可保存xml文件。

项目结构搭建

项目源码：github源码下载地址：git clone https://github.com/qqwweee/keras-yolo3
到yolo官网，下载yolo3预训练权重：https://pjreddie.com/media/files/yolov3.weights 下载后放到根目录
Tiny YOLOv3的权重文件下载：https://pjreddie.com/darknet/yolo/

CPU环境搭建（Ubuntu安装cpu版本tensflow）

#方法1
在https://github.com/lakshayg/tensorflow-build下载   --------》未成功
#方法2
pip install tensorflow-cpu==1.15.0 -i https://pypi.douban.com/simple/  #直接在命令行安装
#方法3（目前在用）
pip install tensorflow==1.12.0 
pip install Keras==2.2.4
pip install Pillow==8.2.0

GPU环境搭建（需要安装gpu版本tensorflow）建议使用GPU训练速度会更快

cuda 8.0
python 3.6
conda install tensorflow-gpu==1.12.0
conda install keras

conda使用

conda create -n zs python=3.6   # 使用python3.6创建你的虚拟环境
conda remove -n zs --all  # 删除虚拟环境zs
conda activate zs #激活环境
deactivate #退出环境

使用默认的模型

python yolo_video.py --image

训练自己的模型构建步骤

标注图片
生成XML 文件
构建自己的数据集目录结构

convert_to_txt.py 文件（执行后生成上述图片）

import os
import random
 
trainval_percent = 0.1
train_percent = 0.9 # 全部划分为训练集，因为yolo3在训练时依旧会划分训练集与测试集，不需要在此划分
xmlfilepath = 'Annotations'
txtsavepath = 'ImageSets/Main'
total_xml = os.listdir(xmlfilepath)
 
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
 
ftrainval = open('ImageSets/Main/trainval.txt', 'w')
ftest = open('ImageSets/Main/test.txt', 'w')
ftrain = open('ImageSets/Main/train.txt', 'w')
fval = open('ImageSets/Main/val.txt', 'w')
 
for i in list:
    name = total_xml[i][:-4] + '\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftest.write(name)
        else:
            fval.write(name)
    else:
        ftrain.write(name)
 
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

转换标注数据文件执行voc_annotation.py（位置在项目的根目录下），转换之前先进行修改成你的要检测的类别classes

import xml.etree.ElementTree as ET
from os import getcwd

sets=[('2007', 'train'), ('2007', 'val'), ('2007', 'test')]

# classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
classes = ["dabao"]   #这里是我定义的大宝，此处修改


def convert_annotation(year, image_id, list_file):
    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
    tree=ET.parse(in_file)
    root = tree.getroot()

    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 = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text))
        list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))

wd = getcwd()

for year, image_set in sets:
    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
    list_file = open('%s_%s.txt'%(year, image_set), 'w')
    for image_id in image_ids:
        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg'%(wd, year, image_id))
        convert_annotation(year, image_id, list_file)
        list_file.write('\n')
    list_file.close()

执行执行voc_annotation.py 后生成的转换标注文件
- 生成转换后的标注文件的位置
- 生成的2007_train.txt 文件数据格式（训练文件）
- 生成的2007_val.txt
创建类别文件my_classses.txt(名字可以自定义)
创建权重文件，需要将darknet版本的yolo model 转换为 Keras model
```
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
```
修改训练配置yolov3.cfg,在此文件中搜索yolo，会有三处匹配，都是相同的更改方式，以第一次匹配举例，三处注释位置，也就是共需改动9个位置

#####修改说明#####
[convolutional]
size=1
stride=1
pad=1
filters=18  # 3*(5+len(classes)) # 我训练一种类别 即 3*(5+1) = 18
activation=linear


[yolo]
mask = 6,7,8
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
classes=1 # 一种类别
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1 # 显存小就改为0

参考修改（可做参考）

val_split = 0.1 # 训练集与测试集划分比例
batch_size = 5 # 每次训练选择样本数
epochs = 300 # 训练三百次

修改训练文件train.py

"""
Retrain the YOLO model for your own dataset.
"""
# 防止显存占用过多
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))

import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping

from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data


def _main():
    annotation_path = '2007_train.txt'   #此处修改成你的训练文件
    log_dir = 'logs/000/'  #此处可以自定义成你的路径
    classes_path = 'model_data/my_classes.txt'  #此处修该成你的配置文件
    anchors_path = 'model_data/yolo_anchors.txt'
    # anchors_path = 'model_data/dabao.txt'
    class_names = get_classes(classes_path)
    num_classes = len(class_names)
    anchors = get_anchors(anchors_path)

    input_shape = (416,416) # multiple of 32, hw

    is_tiny_version = len(anchors)==6 # default setting
    if is_tiny_version:
        model = create_tiny_model(input_shape, anchors, num_classes,
            freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')
    else:
        model = create_model(input_shape, anchors, num_classes,
            freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze

    logging = TensorBoard(log_dir=log_dir)
    checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
        monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
    early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)

    val_split = 0.25  #此处可以根据你的数据集量做修改
    with open(annotation_path) as f:
        lines = f.readlines()
    np.random.seed(10101)
    np.random.shuffle(lines)
    np.random.seed(None)
    num_val = int(len(lines)*val_split)
    num_train = len(lines) - num_val

    # Train with frozen layers first, to get a stable loss.
    # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
    if True:
        model.compile(optimizer=Adam(lr=1e-3), loss={
            # use custom yolo_loss Lambda layer.
            'yolo_loss': lambda y_true, y_pred: y_pred})

        batch_size = 4   #此处可以根据你的情况调整，调整显存的,显存小的可以调小
        print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
        model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
                steps_per_epoch=max(1, num_train//batch_size),
                validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
                validation_steps=max(1, num_val//batch_size),
                epochs=50,
                initial_epoch=0,
                callbacks=[logging, checkpoint])
        model.save_weights(log_dir + 'trained_weights_stage_1.h5')

    # Unfreeze and continue training, to fine-tune.
    # Train longer if the result is not good.
    if True:
        for i in range(len(model.layers)):
            model.layers[i].trainable = True
        model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
        print('Unfreeze all of the layers.')

        batch_size = 4 # note that more GPU memory is required after unfreezing the body
        print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
        model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
            steps_per_epoch=max(1, num_train//batch_size),
            validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
            validation_steps=max(1, num_val//batch_size),
            epochs=100,
            initial_epoch=50,
            callbacks=[logging, checkpoint, reduce_lr, early_stopping])
        model.save_weights(log_dir + 'trained_weights_final.h5')  # 训练生成的模型,log_dir在前面可以自定义成你的存放位置

    # Further training if needed.


def get_classes(classes_path):
    '''loads the classes'''
    with open(classes_path) as f:
        class_names = f.readlines()
    class_names = [c.strip() for c in class_names]
    return class_names

def get_anchors(anchors_path):
    '''loads the anchors from a file'''
    with open(anchors_path) as f:
        anchors = f.readline()
    anchors = [float(x) for x in anchors.split(',')]
    return np.array(anchors).reshape(-1, 2)


def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
            weights_path='model_data/yolo_weights.h5'):
    '''create the training model'''
    K.clear_session() # get a new session
    image_input = Input(shape=(None, None, 3))
    h, w = input_shape
    num_anchors = len(anchors)

    y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
        num_anchors//3, num_classes+5)) for l in range(3)]

    model_body = yolo_body(image_input, num_anchors//3, num_classes)
    print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))

    if load_pretrained:
        model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
        print('Load weights {}.'.format(weights_path))
        if freeze_body in [1, 2]:
            # Freeze darknet53 body or freeze all but 3 output layers.
            num = (185, len(model_body.layers)-3)[freeze_body-1]
            for i in range(num): model_body.layers[i].trainable = False
            print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))

    model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
        arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
        [*model_body.output, *y_true])
    model = Model([model_body.input, *y_true], model_loss)

    return model

def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
            weights_path='model_data/tiny_yolo_weights.h5'):
    '''create the training model, for Tiny YOLOv3'''
    K.clear_session() # get a new session
    image_input = Input(shape=(None, None, 3))
    h, w = input_shape
    num_anchors = len(anchors)

    y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \
        num_anchors//2, num_classes+5)) for l in range(2)]

    model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)
    print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))

    if load_pretrained:
        model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
        print('Load weights {}.'.format(weights_path))
        if freeze_body in [1, 2]:
            # Freeze the darknet body or freeze all but 2 output layers.
            num = (20, len(model_body.layers)-2)[freeze_body-1]
            for i in range(num): model_body.layers[i].trainable = False
            print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))

    model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
        arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(
        [*model_body.output, *y_true])
    model = Model([model_body.input, *y_true], model_loss)

    return model

def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
    '''data generator for fit_generator'''
    n = len(annotation_lines)
    i = 0
    while True:
        image_data = []
        box_data = []
        for b in range(batch_size):
            if i==0:
                np.random.shuffle(annotation_lines)
            image, box = get_random_data(annotation_lines[i], input_shape, random=True)
            image_data.append(image)
            box_data.append(box)
            i = (i+1) % n
        image_data = np.array(image_data)
        box_data = np.array(box_data)
        y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
        yield [image_data, *y_true], np.zeros(batch_size)

def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes):
    n = len(annotation_lines)
    if n==0 or batch_size<=0: return None
    return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)

if __name__ == '__main__':
    _main()

训练模型

#  运行train.py，进行训练模型
python train.py

YOLOV3目标检测框架搭建和训练自己模型的流程

使用模型完成模型的训练之后，调用yolo.py即可使用我们训练好的模型

修改yolo.py用你训练模型

# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
"""
# 防止显存占用过多
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))

import colorsys
import os
from timeit import default_timer as timer

import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw

from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
import os
from keras.utils import multi_gpu_model

class YOLO(object):
    _defaults = {
        # "model_path": 'model_data/yolo.h5',
        "model_path": 'trained_weights_final.h5',  # 此处修改成你的训练模型
        "anchors_path": 'model_data/yolo_anchors.txt',
        # "classes_path": 'model_data/coco_classes.txt',
        "classes_path": 'model_data/my_classes.txt',  # 此处修改成你的类别
        "score" : 0.3,   # 此处可根据你的识别情况做修改
        "iou" : 0.45,
        "model_image_size": (416, 416),
        "gpu_num" : 1,
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults) # set up default values
        self.__dict__.update(kwargs) # and update with user overrides
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.boxes, self.scores, self.classes = self.generate()

    def _get_class(self):
        classes_path = os.path.expanduser(self.classes_path)
        with open(classes_path) as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names

    def _get_anchors(self):
        anchors_path = os.path.expanduser(self.anchors_path)
        with open(anchors_path) as f:
            anchors = f.readline()
        anchors = [float(x) for x in anchors.split(',')]
        return np.array(anchors).reshape(-1, 2)

    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'

        # Load model, or construct model and load weights.
        num_anchors = len(self.anchors)
        num_classes = len(self.class_names)
        is_tiny_version = num_anchors==6 # default setting
        try:
            self.yolo_model = load_model(model_path, compile=False)
        except:
            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
        else:
            assert self.yolo_model.layers[-1].output_shape[-1] == \
                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                'Mismatch between model and given anchor and class sizes'

        print('{} model, anchors, and classes loaded.'.format(model_path))

        # Generate colors for drawing bounding boxes.
        hsv_tuples = [(x / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
        self.colors = list(
            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
                self.colors))
        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
        np.random.seed(None)  # Reset seed to default.

        # Generate output tensor targets for filtered bounding boxes.
        self.input_image_shape = K.placeholder(shape=(2, ))
        if self.gpu_num>=2:
            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                len(self.class_names), self.input_image_shape,
                score_threshold=self.score, iou_threshold=self.iou)
        return boxes, scores, classes

    def detect_image(self, image):
        start = timer()

        if self.model_image_size != (None, None):
            assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
            assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
        else:
            new_image_size = (image.width - (image.width % 32),
                              image.height - (image.height % 32))
            boxed_image = letterbox_image(image, new_image_size)
        image_data = np.array(boxed_image, dtype='float32')

        print(image_data.shape)
        image_data /= 255.
        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

        out_boxes, out_scores, out_classes = self.sess.run(
            [self.boxes, self.scores, self.classes],
            feed_dict={
                self.yolo_model.input: image_data,
                self.input_image_shape: [image.size[1], image.size[0]],
                K.learning_phase(): 0
            })

        print('Found {} boxes for {}'.format(len(out_boxes), 'img'))

        font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                    size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
        thickness = (image.size[0] + image.size[1]) // 300

        for i, c in reversed(list(enumerate(out_classes))):
            predicted_class = self.class_names[c]
            box = out_boxes[i]
            score = out_scores[i]

            label = '{} {:.2f}'.format(predicted_class, score)
            draw = ImageDraw.Draw(image)
            label_size = draw.textsize(label, font)

            top, left, bottom, right = box
            top = max(0, np.floor(top + 0.5).astype('int32'))
            left = max(0, np.floor(left + 0.5).astype('int32'))
            bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
            right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
            print(label, (left, top), (right, bottom))

            if top - label_size[1] >= 0:
                text_origin = np.array([left, top - label_size[1]])
            else:
                text_origin = np.array([left, top + 1])

            # My kingdom for a good redistributable image drawing library.
            for i in range(thickness):
                draw.rectangle(
                    [left + i, top + i, right - i, bottom - i],
                    outline=self.colors[c])
            draw.rectangle(
                [tuple(text_origin), tuple(text_origin + label_size)],
                fill=self.colors[c])
            draw.text(text_origin, label, fill=(0, 0, 0), font=font)
            del draw

        end = timer()
        print(end - start)
        return image

    def close_session(self):
        self.sess.close()

def detect_video(yolo, video_path, output_path=""):
    import cv2
    vid = cv2.VideoCapture(video_path)
    if not vid.isOpened():
        raise IOError("Couldn't open webcam or video")
    video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))
    video_fps       = vid.get(cv2.CAP_PROP_FPS)
    video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
                        int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    isOutput = True if output_path != "" else False
    if isOutput:
        print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
        out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
    accum_time = 0
    curr_fps = 0
    fps = "FPS: ??"
    prev_time = timer()
    while True:
        return_value, frame = vid.read()
        image = Image.fromarray(frame)
        image = yolo.detect_image(image)
        result = np.asarray(image)
        curr_time = timer()
        exec_time = curr_time - prev_time
        prev_time = curr_time
        accum_time = accum_time + exec_time
        curr_fps = curr_fps + 1
        if accum_time > 1:
            accum_time = accum_time - 1
            fps = "FPS: " + str(curr_fps)
            curr_fps = 0
        cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=0.50, color=(255, 0, 0), thickness=2)
        cv2.namedWindow("result", cv2.WINDOW_NORMAL)
        cv2.imshow("result", result)
        if isOutput:
            out.write(result)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    yolo.close_session()

if __name__ == '__main__':
    yolo=YOLO()
    path = './img/1.jpg'
    try:
        image = Image.open(path)
    except:
        print('Open Error! Try again!')
    else:
        r_image = yolo.detect_image(image)
        r_image.save("./img/cup2.jpg")
        r_image.show()
    yolo.close_session()

测试模型

import sys
import argparse
from yolo import YOLO, detect_video
from PIL import Image


if __name__ == '__main__':
    config = {
        "model_path": "logs/000/trained_weights_final.h5", # 加载模型(你的模型)
        "score": 0.3, # 超出这个值的预测才会被显示
        "iou": 0.5, # 交并比
    }
    yolo = YOLO(**config)
    image = Image.open("./img/dabao1.jpg")
    r_image = yolo.detect_image(image)
    r_image.save("./img/dabao1_detect.jpg")

效果展示

项目实践参考链接

https://pjreddie.com/darknet/yolo/   #YOLO官网
# 以下是文章参考博文,感谢以下博主分享
https://blog.csdn.net/qinchang1/article/details/89608058 
https://my.oschina.net/u/876354/blog/1927881
https://www.it610.com/article/1277379316287553536.htm
https://www.cnblogs.com/WindrunnerMax/p/12782939.html

关于显存问题（占用过多）可参考这个链接（在GPU环境中）

https://blog.csdn.net/sinat_26917383/article/details/75633754  # 感谢博主分享

# 在使用keras时候会出现总是占满GPU显存的情况，可以通过重设backend的GPU占用情况来进行调节。
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))

tensorflow + CPU充分使用（cpu环境中）

num_cores = 4

config = tf.ConfigProto(intra_op_parallelism_threads=num_cores, inter_op_parallelism_threads=num_cores,
                        allow_soft_placement=True, device_count={'CPU': 4})
session = tf.Session(config=config)
K.set_session(session)

'''
# 说明
device_count, 告诉tf Session使用CPU数量上限，如果你的CPU数量较多，可以适当加大这个值
inter_op_parallelism_threads和intra_op_parallelism_threads告诉session操作的线程并行程度，如果值越小，线程的复用就越少，越可能使用较多的CPU核数。如果值为0，TF会自动选择一个合适的值。
allow_soft_placement=True， 有时候，不同的设备，它的cpu和gpu是不同的，如果将这个选项设置成True，那么当运行设备不满足要求时，会自动分配GPU或者CPU。
'''

ons.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))


- #### tensorflow + CPU充分使用（cpu环境中）

```python
num_cores = 4

config = tf.ConfigProto(intra_op_parallelism_threads=num_cores, inter_op_parallelism_threads=num_cores,
                        allow_soft_placement=True, device_count={'CPU': 4})
session = tf.Session(config=config)
K.set_session(session)

'''
# 说明
device_count, 告诉tf Session使用CPU数量上限，如果你的CPU数量较多，可以适当加大这个值
inter_op_parallelism_threads和intra_op_parallelism_threads告诉session操作的线程并行程度，如果值越小，线程的复用就越少，越可能使用较多的CPU核数。如果值为0，TF会自动选择一个合适的值。
allow_soft_placement=True， 有时候，不同的设备，它的cpu和gpu是不同的，如果将这个选项设置成True，那么当运行设备不满足要求时，会自动分配GPU或者CPU。
'''

码农公寓

YOLOV3目标检测框架搭建和训练自己模型的步骤（超详细）

Ubuntu18.04安装labelImg标注工具—pyqt5 安装步骤

使用图像标注工具

项目结构搭建

CPU环境搭建（Ubuntu安装cpu版本tensflow）

GPU环境搭建（需要安装gpu版本tensorflow）建议使用GPU训练速度会更快

conda使用

使用默认的模型

训练自己的模型构建步骤

标注图片

生成XML 文件

构建自己的数据集目录结构

convert_to_txt.py 文件 （执行后生成上述图片）

转换标注数据文件 执行voc_annotation.py（位置在项目的根目录下），转换之前先进行修改成你的要检测的类别classes

执行执行voc_annotation.py 后生成的转换标注文件

创建类别文件my_classses.txt(名字可以自定义)

创建权重文件，需要将darknet版本的yolo model 转换为 Keras model

修改训练配置yolov3.cfg,在此文件中搜索yolo，会有三处匹配，都是相同的更改方式，以第一次匹配举例，三处注释位置，也就是共需改动9个位置

参考修改（可做参考）

修改训练文件train.py

训练模型

使用模型 完成模型的训练之后，调用yolo.py即可使用我们训练好的模型

项目实践参考链接

关于显存问题（占用过多）可参考这个链接（在GPU环境中）

tensorflow + CPU充分使用（cpu环境中）

相关文章

convert_to_txt.py 文件（执行后生成上述图片）

转换标注数据文件执行voc_annotation.py（位置在项目的根目录下），转换之前先进行修改成你的要检测的类别classes

使用模型完成模型的训练之后，调用yolo.py即可使用我们训练好的模型