代码:https://github.com/qqwweee/keras-yolo3
修改yolov3.cfg文件:https://blog.csdn.net/lilai619/article/details/79695109
写文章不易,转载请表明本文出处:https://blog.csdn.net/Patrick_Lxc/article/details/80615433
本文介绍如何制作数据集、修改代码、不加载预权重从头跑自己的训练数据
一、简单回顾一下yolo原理:
1、端到端,输入图像,一次性输出每个栅格预测的一种或多种物体
2、坐标x,y代表了预测的bounding box的中心与栅格边界的相对值。
坐标w,h代表了预测的bounding box的width、height相对于整幅图像(或者栅格)width,height的比例。
3、
4、
考虑各项权重:λcoord = 5, λnoobj = 0.5。因为不包含物体的框较多,需要弱化对应的权重影响,不然会导致包含物体的框贡献低,训练不稳定甚至发散。
5、如果想一个格子预测多个类别,需要Anchors. --yolo2
像这样:
像这样:
工具:LabelImg ,链接:https://pan.baidu.com/s/1GJFYcFm5Zlb-c6tIJ2N4hw 密码:h0i5
像这样:
像这样:
test.py代码:
import osimport randomtrainval_percent = 0.1train_percent = 0.9xmlfilepath = '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()
VOC2007数据集制作完成,但是,yolo3并不直接用这个数据集,开心么?
需要的运行voc_annotation.py ,classes以三个颜色为例,你的数据集记得改
运行之后,会在主目录下多生成三个txt文件,
像这样:
手动删除2007_,
注明一下,这个文件是用于转换官网下载的.weights文件用的。训练自己的网络并不需要去管他。详见readme
IDE里直接打开cfg文件,ctrl+f搜 yolo, 总共会搜出3个含有yolo的地方,睁开你的卡姿兰大眼睛,3个yolo!!
每个地方都要改3处,filters:3*(5+len(classes));
classes: len(classes) = 3,这里以红、黄、蓝三个颜色为例
random:原来是1,显存小改为0
第七步:修改model_data下的文件,放入你的类别,coco,voc这两个文件都需要修改。
像这样:
为什么说这篇文章是从头开始训练?代码原作者在train.py做了两件事情:
1、会加载预先对coco数据集已经训练完成的yolo3权重文件,
像这样:
2、冻结了开始到最后倒数第N层(源代码为N=-2),
像这样:
但是,你和我想训练的东西,coco里没有啊,所以,就干脆从头开始训练吧
对train.py做了一下修改,直接复制替换原文件就可以了,细节大家自己看吧,直接运行,loss达到10几的时候效果就可以了
train.py:
"""Retrain the YOLO model for your own dataset."""import numpy as npimport keras.backend as Kfrom keras.layers import Input, Lambdafrom keras.models import Modelfrom keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStoppingfrom yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_lossfrom yolo3.utils import get_random_datadef _main(): annotation_path = 'train.txt' log_dir = 'logs/000/' classes_path = 'model_data/voc_classes.txt' anchors_path = 'model_data/yolo_anchors.txt' class_names = get_classes(classes_path) anchors = get_anchors(anchors_path) input_shape = (416,416) # multiple of 32, hw model = create_model(input_shape, anchors, len(class_names) ) train(model, annotation_path, input_shape, anchors, len(class_names), log_dir=log_dir)def train(model, annotation_path, input_shape, anchors, num_classes, log_dir='logs/'): model.compile(optimizer='adam', loss={ 'yolo_loss': lambda y_true, y_pred: y_pred}) 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=1) batch_size = 10 val_split = 0.1 with open(annotation_path) as f: lines = f.readlines() np.random.shuffle(lines) num_val = int(len(lines)*val_split) num_train = len(lines) - num_val print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrap(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrap(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=500, initial_epoch=0) model.save_weights(log_dir + 'trained_weights.h5')def get_classes(classes_path): with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_namesdef get_anchors(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 create_model(input_shape, anchors, num_classes, load_pretrained=False, freeze_body=False, weights_path='model_data/yolo_weights.h5'): 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: # Do not freeze 3 output layers. num = len(model_body.layers)-7 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 modeldef data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes): n = len(annotation_lines) np.random.shuffle(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): i %= n image, box = get_random_data(annotation_lines[i], input_shape, random=True) image_data.append(image) box_data.append(box) i += 1 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_wrap(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()
'''def detect_img(yolo): while True: img = input('Input image filename:') try: image = Image.open(img) except: print('Open Error! Try again!') continue else: r_image = yolo.detect_image(image) r_image.show() yolo.close_session()'''import globdef detect_img(yolo): path = "D:\VOCdevkit\VOC2007\JPEGImages\*.jpg" outdir = "D:\\VOCdevkit\VOC2007\SegmentationClass" for jpgfile in glob.glob(path): img = Image.open(jpgfile) img = yolo.detect_image(img) img.save(os.path.join(outdir, os.path.basename(jpgfile))) yolo.close_session()
明天写yolo2和yolo3的具体原理。立牌坊。。。又开始打嗝了,醉了,一直打着嗝写完了这篇介绍,想起高中的时候,最长时间打嗝打了两天,想死。
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网站禁止我给你评论回复了, 你看这,6.25回复:
先声明一下,quick start的步骤:
1、Download YOLOv3 weights from YOLO website.
2、Convert the Darknet YOLO model to a Keras model.(因为官网给出的是darknet的权重文件,所以需要转换成Keras需要的形式)
3、Run YOLO detection.
依次对应:
1、wget https://pjreddie.com/media/files/yolov3.weights
2、python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
3、python yolo.py OR python yolo_video.py [video_path] [output_path(optional)]
理解以上的步骤之后,回答您的问题:
对于已经存在于coco数据集80个种类之中的一类,就不要自己训练了,官网权重训练的很好了已经;
对于不存在coco数据集的一种,无视convert.py, 无视.cfg文件,不要预加载官方权重,直接用我的train.py代码进行训练就可以了。你预加载官方权重,再去训练一个全新的物种,个人认为是浪费资源完全没意义的
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