CSDN博客地址：https://blog.csdn.net/sinat_26917383/article/details/85614247 其中，
- selfdata_keras_yolov3.ipynb，自己训练时候的ipynb
- selfdata_yolov3_test.ipynb，自己预测时候的ipynb
- yolo_matt.py,预测时候改进输出结果
最简单是因为把数据整理成以下的样子就可以开始训练：
path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
path/to/img2.jpg 120,300,250,600,2
...
也就是：地址，xmin,ymin,xmax,ymax，类别ID然后空格下一个box，每张图一行。 例子：
images/images_all/86900fb6gy1fl4822o7qmj22ao328qv7.jpg 10,259,399,580,27
images/images_all/b95fe9cbgw1eyw88vlifjj20c70hsq46.jpg 10,353,439,640,29
images/images_all/005CsCZ0jw1f1n8kcj8m1j30ku0kumz6.jpg 75,141,343,321,27
keras源码中有两段训练：
- 第一段冻结前面的249层进行迁移学习（原有的yolov3）
- 第二段解冻全部层进行训练
笔者自己的训练数据集是专业领域的图像，所以基本第一阶段的迁移学习阶段没啥用，因为与原有的yolov3训练集差异太大，如果你也是，请直接开始第二段或者重新根据darknet53训练。 那么这边就有三样可能需要预下载的模型：
- yolo_weights.h5 预训练模型（用作迁移）
python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5 - darknet53.weights （用作重新训练）
wget https://pjreddie.com/media/files/darknet53.conv.74 - yolo.h5 （yolov3-VOC训练模型，可以直接用来做预测 ）
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
来看看训练时候需要的参数：
class yolo_args:
annotation_path = 'train.txt'
log_dir = 'logs/003/'
classes_path = 'model_data/class_file_en.txt'
anchors_path = 'model_data/yolo_anchors.txt'
input_shape = (416,416) # multiple of 32, hw
# 608*608 416*416 320*320
val_split = 0.1
batch_size = 16
epochs_stage_1 = 10
stage_1_train = False
epochs_finally = 100
finally_train = True
weights_path = 'logs/003/ep009-loss33.297-val_loss32.851.h5'# 可以使用'model_data/tiny_yolo_weights.h5' 也可以使用tiny_yolo的：'model_data/yolo_weights.h5'
# train
_main(yolo_args)
-
annotation_path就是数据集准备的txt
-
log_dir ，Model存放地址，譬如：
events.out.tfevents.1545966202、ep077-loss19.318-val_loss19.682.h5 -
classes_path ，分类内容
-
anchors_path ，yolo anchors，可自行调整，也可以使用默认的
-
input_shape ，一般是416
-
epochs_stage_1 = 10和stage_1_train = False，是同一个，也就是是否进行迁移学习（stage_1_train），要学习的话，学习几个epoch（epochs_stage_1） -
epochs_finally = 100和finally_train = True，是，是否进行后面开放所有层的学习（finally_train），学习几个epoch（epochs_finally） -
weights_path ，调用model的路径
这里需要注意： 如果要在之前训练基础上，追加训练，一般要把batch_size设置小一些，然后加载之前的权重。
来看一个简单的预测
import sys
import argparse
from yolo import YOLO, detect_video
from PIL import Image
yolo_test_args = {
"model_path": 'model_data/yolo.h5',
"anchors_path": 'model_data/yolo_anchors.txt',
"classes_path": 'model_data/coco_classes.txt',
"score" : 0.3,
"iou" : 0.45,
"model_image_size" : (416, 416),
"gpu_num" : 1,
}
yolo_test = YOLO(**yolo_test_args)
image = Image.open('images/part1/path1.jpg')
r_image = yolo_test.detect_image(image)
r_image.show()
直接返回的是带框的图片，如果你要输出boxes，可以自己改一下detect_image函数。
此时注意以下：out_boxes, out_scores, out_classes中out_boxes，每个Boxes代表的是：y_min, x_min, y_max, x_max
在此之上，进行预测结果优化，可参考：yolo_matt.py:
import sys
import argparse
from yolo_matt import YOLO, detect_video
from PIL import Image
yolo_test_args = {
"model_path": 'logs/003/ep077-loss19.318-val_loss19.682.h5',
"anchors_path": 'model_data/yolo_anchors.txt',
"classes_path": 'model_data/class_file_en.txt',
"score" : 0.2,# 0.2
"iou" : 0.1,# 0.45
"model_image_size" : (416, 416),
"gpu_num" : 1,
}
yolo_test = YOLO(**yolo_test_args)
# 输出内容整理
def _get_class(classes_path):
classes_path = os.path.expanduser(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 yolov3_output(image,out_boxes,out_scores,out_classes):
output = []
yolo_classes = _get_class(yolo_test_args['classes_path'])
for n,box in enumerate(out_boxes):
y_min, x_min, y_max, x_max = box
y_min = max(0, np.floor(y_min + 0.5).astype('int32'))
x_min = max(0, np.floor(x_min + 0.5).astype('int32'))
y_max = min(image.size[1], np.floor(y_max + 0.5).astype('int32'))
x_max = min(image.size[0], np.floor(x_max + 0.5).astype('int32'))
score = out_scores[n]
yo_class = yolo_classes[out_classes[n]]
output.append({ 'y_min':y_min, 'x_min':x_min, 'y_max':y_max, 'x_max':x_max,\
'width':image.size[0],'height':image.size[1],\
'score':score,'yo_class':yo_class})
return output
image = Image.open('images/images_all/path1.jpg')
r_image,out_boxes, out_scores, out_classes = yolo_test.detect_image(image)
output = yolov3_output(r_image,out_boxes,out_scores,out_classes)
输出结果类似：
{
'path1.jpg':
[{'y_min': 416, 'x_min': 34, 'y_max': 754, 'x_max': 367, 'width': 440, 'height': 783, 'score': 0.9224778, 'yo_class': 'class1'},
{'y_min': 428, 'x_min': 3, 'y_max': 783, 'x_max': 352, 'width': 440, 'height': 783, 'score': 0.2180994, 'yo_class': 'class2'}]
}
########## 原项目内容 20190102 #############
A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.
- Download YOLOv3 weights from YOLO website.
- Convert the Darknet YOLO model to a Keras model.
- Run YOLO detection.
wget https://pjreddie.com/media/files/yolov3.weights
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
python yolo_video.py [OPTIONS...] --image, for image detection mode, OR
python yolo_video.py [video_path] [output_path (optional)]
For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with --model model_file and --anchors anchor_file.
Use --help to see usage of yolo_video.py:
usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
[--classes CLASSES] [--gpu_num GPU_NUM] [--image]
[--input] [--output]
positional arguments:
--input Video input path
--output Video output path
optional arguments:
-h, --help show this help message and exit
--model MODEL path to model weight file, default model_data/yolo.h5
--anchors ANCHORS path to anchor definitions, default
model_data/yolo_anchors.txt
--classes CLASSES path to class definitions, default
model_data/coco_classes.txt
--gpu_num GPU_NUM Number of GPU to use, default 1
--image Image detection mode, will ignore all positional arguments
- MultiGPU usage: use
--gpu_num Nto use N GPUs. It is passed to the Keras multi_gpu_model().
-
Generate your own annotation file and class names file.
One row for one image;
Row format:image_file_path box1 box2 ... boxN;
Box format:x_min,y_min,x_max,y_max,class_id(no space).
For VOC dataset, trypython voc_annotation.py
Here is an example:path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3 path/to/img2.jpg 120,300,250,600,2 ... -
Make sure you have run
python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
The file model_data/yolo_weights.h5 is used to load pretrained weights. -
Modify train.py and start training.
python train.py
Use your trained weights or checkpoint weights with command line option--model model_filewhen using yolo_video.py Remember to modify class path or anchor path, with--classes class_fileand--anchors anchor_file.
If you want to use original pretrained weights for YOLOv3:
1. wget https://pjreddie.com/media/files/darknet53.conv.74
2. rename it as darknet53.weights
3. python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5
4. use model_data/darknet53_weights.h5 in train.py
-
The test environment is
- Python 3.5.2
- Keras 2.1.5
- tensorflow 1.6.0
-
Default anchors are used. If you use your own anchors, probably some changes are needed.
-
The inference result is not totally the same as Darknet but the difference is small.
-
The speed is slower than Darknet. Replacing PIL with opencv may help a little.
-
Always load pretrained weights and freeze layers in the first stage of training. Or try Darknet training. It's OK if there is a mismatch warning.
-
The training strategy is for reference only. Adjust it according to your dataset and your goal. And add further strategy if needed.
-
For speeding up the training process with frozen layers train_bottleneck.py can be used. It will compute the bottleneck features of the frozen model first and then only trains the last layers. This makes training on CPU possible in a reasonable time. See this for more information on bottleneck features.