这是一个mobilenet-yolov4-lite的库，把yolov4主干网络修改成了mobilenet，修改了Panet的卷积组成，使参数量大幅度缩小。

Related tags

Deep Learning mobilenet-yolov4-lite-keras
Overview

YOLOV4：You Only Look Once目标检测模型-修改mobilenet系列主干网络-在Keras当中的实现

2021年2月8日更新：
加入letterbox_image的选项，关闭letterbox_image后网络的map一般可以得到提升。

目录

  1. 性能情况 Performance
  2. 所需环境 Environment
  3. 注意事项 Attention
  4. 小技巧的设置 TricksSet
  5. 文件下载 Download
  6. 预测步骤 How2predict
  7. 训练步骤 How2train
  8. 参考资料 Reference

性能情况

训练数据集 权值文件名称 测试数据集 输入图片大小 mAP 0.5:0.95 mAP 0.5
VOC07+12 yolov4_mobilenet_v1_025_voc.h5 VOC-Test07 416x416 - 66.29
VOC07+12 yolov4_mobilenet_v1_voc.h5 VOC-Test07 416x416 - 80.18
VOC07+12 yolov4_mobilenet_v2_voc.h5 VOC-Test07 416x416 - 79.72
VOC07+12 yolov4_mobilenet_v3_voc.h5 VOC-Test07 416x416 - 78.45

所需环境

tensorflow-gpu==1.13.1
keras==2.1.5

注意事项

提供的四个训练好的权重分别是基于mobilenetv1-025、mobilenetv1、mobilenetv2、mobilenetv3主干网络训练而成的。使用的时候注意backbone和权重的对应。
训练前注意修改alpha、model_path和backbone使得三者对应。
预测前注意修改alhpa、model_path和backbone使得三者对应。

小技巧的设置

在train.py文件下：
1、mosaic参数可用于控制是否实现Mosaic数据增强。
2、Cosine_scheduler可用于控制是否使用学习率余弦退火衰减。
3、label_smoothing可用于控制是否Label Smoothing平滑。

文件下载

训练所需的各个权值可在百度网盘中下载。
链接: https://pan.baidu.com/s/1CA4d9gVBI5JObnhK_xRPxQ 提取码: 9am5
四个已经训练好的权重均为VOC数据集的权重。

预测步骤

a、使用预训练权重

  1. 下载完库后解压，在百度网盘下载权重，放入model_data，运行predict.py，输入
img/street.jpg
  1. 利用video.py可进行摄像头检测。

b、使用自己训练的权重

  1. 按照训练步骤训练。
  2. 在yolo.py文件里面，在如下部分修改model_path、alpha和classes_path使其对应训练好的文件；model_path对应logs文件夹下面的权值文件，alpha是通道的缩放比例，classes_path是model_path对应分的类。
_defaults = {
    "model_path"        : 'model_data/yolov4_mobilenet_v1_voc.h5',
    "anchors_path"      : 'model_data/yolo_anchors.txt',
    "classes_path"      : 'model_data/voc_classes.txt',
    "backbone"          : 'mobilenetv1',
    "alpha"             : 1,
    "score"             : 0.5,
    "iou"               : 0.3,
    "max_boxes"         : 100,
    # 显存比较小可以使用416x416
    # 显存比较大可以使用608x608
    "model_image_size"  : (416, 416)
}
  1. 运行predict.py，输入
img/street.jpg
  1. 利用video.py可进行摄像头检测。

训练步骤

  1. 本文使用VOC格式进行训练。
  2. 训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的Annotation中。
  3. 训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。
  4. 在训练前利用voc2yolo4.py文件生成对应的txt。
  5. 再运行根目录下的voc_annotation.py，运行前需要将classes改成你自己的classes。注意不要使用中文标签，文件夹中不要有空格！
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
  1. 此时会生成对应的2007_train.txt，每一行对应其图片位置及其真实框的位置。
  2. 在训练前需要务必在model_data下新建一个txt文档，文档中输入需要分的类，在train.py中将classes_path指向该文件，示例如下：
classes_path = 'model_data/new_classes.txt'

model_data/new_classes.txt文件内容为：

cat
dog
...
  1. 运行train.py即可开始训练。

mAP目标检测精度计算更新

更新了get_gt_txt.py、get_dr_txt.py和get_map.py文件。
get_map文件克隆自https://github.com/Cartucho/mAP
具体mAP计算过程可参考：https://www.bilibili.com/video/BV1zE411u7Vw

Reference

https://github.com/qqwweee/keras-yolo3/
https://github.com/Cartucho/mAP
https://github.com/Ma-Dan/keras-yolo4

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Comments
  • Project dependencies may have API risk issues

    Project dependencies may have API risk issues

    Hi, In mobilenet-yolov4-keras, inappropriate dependency versioning constraints can cause risks.

    Below are the dependencies and version constraints that the project is using

    scipy==1.2.1
numpy==1.17.0
Keras==2.1.5
matplotlib==3.1.2
opencv_python==4.1.2.30
tensorflow_gpu==1.13.2
tqdm==4.60.0
Pillow==8.2.0
h5py==2.10.0

    The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict. The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

    After further analysis, in this project, The version constraint of dependency matplotlib can be changed to >=1.3.0,<=3.0.3. The version constraint of dependency tqdm can be changed to >=4.36.0,<=4.64.0. The version constraint of dependency Pillow can be changed to ==9.2.0. The version constraint of dependency Pillow can be changed to >=2.0.0,<=9.1.1.

    The above modification suggestions can reduce the dependency conflicts as much as possible, and introduce the latest version as much as possible without calling Error in the projects.

    The invocation of the current project includes all the following methods.

    The calling methods from the matplotlib 
    matplotlib.use
    The calling methods from the tqdm 
    tqdm.tqdm
    The calling methods from the Pillow 
    PIL.Image.fromarray
PIL.ImageFont.truetype
PIL.Image.open
PIL.Image.new
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xml.endswith
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self.get_random_data_with_Mosaic
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kmeans
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total_xml.append
self.yolo_model.load_weights
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tensorflow.image.non_max_suppression
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os.path.splitext
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matplotlib.pyplot.gca.set_xlim
image_set.year.VOCdevkit_path.os.path.join.open.read
glob.glob1
matplotlib.pyplot.fill_between
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cv2.VideoCapture.read
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matplotlib.pyplot.gcf.get_figheight
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os.path.isfile
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conv_name_base.filters1.Conv2D
keras.optimizers.SGD
cv2.VideoWriter_fourcc
matplotlib.pyplot.text
keras.backend.int_shape
matplotlib.pyplot.xlabel
weight_decay.num_classes.anchors_mask.len.DarknetConv2D
numpy.floor
self.WarmUpCosineDecayScheduler.super.__init__
near.last_clu.all
yolo.YOLO
block_id.relu6.Activation
detection.split
keras.backend.exp
file_lines_to_list
fp_sorted.append
len
numpy.reshape
prefix.relu6.Activation
already_seen_classes.append
obj.findtext
dict
i.j.List1.rjust
new_f.write
matplotlib.pyplot.show
keras.callbacks.LearningRateScheduler
conv_block
i_list.append
tensorflow.cast
numpy.insert
matplotlib.pyplot.gca.get_xlim
class_names.index
cv2.imshow
prec.insert
pycocotools.coco.COCO
strides.kernel.filters.Conv2D
numpy.minimum
tensorflow.logging.set_verbosity
VOCdevkit_path.os.path.join.open.read.strip
prefix.exp_size.Conv2D
image_id.VOCdevkit_path.os.path.join.ET.parse.getroot.findall
get_anchors_and_decode
cv2.cvtColor
pow
yolo.detect_image.append
transition_block
cv2.VideoCapture
obj.find
image.resize.resize
label.encode.encode
matplotlib.pyplot.gcf.tight_layout
keras.backend.reshape
convert_annotation
nets.yolo_training.get_lr_scheduler
numpy.max
matplotlib.pyplot.barh
pycocotools.coco.COCO.loadRes
cv2.destroyAllWindows
format.Add
matplotlib.pyplot.figure
_smooth_labels
numpy.shape
keras.backend.clear_session
obj.split
no_bias_kwargs.update
image.resize.convert
shutil.rmtree
keras.backend.min
self.losses.append
PIL.Image.open.save
keras.backend.learning_phase
_make_divisible.Conv2D
gamma.alpha.focal_loss.label_smoothing.num_classes.input_shape.input_shape.num_classes.anchors_mask.anchors.input_shape.yolo_loss.Lambda
l.get_output_shape_at
self.model_body.get_output_at
matplotlib.pyplot.gcf
format.BatchNormalization
anchors.np.array.reshape
random.shuffle
yolo.YOLO.detect_image
matplotlib.pyplot.close
yolo.YOLO.get_FPS
MobileNetV1
prefix.out_channels.Conv2D
nets.yolo.get_train_model.fit_generator
keras.backend.concatenate.append
math.ceil
img_name.lower.endswith
rec.append
str.astype
format.Activation
utils_map.get_map
block_id.BatchNormalization
keras.backend.expand_dims.stack
matplotlib.pyplot.cla
numpy.random.uniform
pycocotools.cocoeval.COCOeval.accumulate
part.str.sub_block_id.str.block_id.str.BatchNormalization
numpy.expand_dims
weight_decay.num_filters.DarknetConv2D_BN_Leaky
matplotlib.pyplot.gca.set_ylim
numpy.arange
pycocotools.cocoeval.COCOeval
make_five_convs
keras.layers.MaxPooling2D
keras.backend.set_value
prec.append
preprocess_gt
conv_name_base.kernel_size.filters2.Conv2D
c.strip
matplotlib.pyplot.gcf.get_figwidth
numpy.logical_and
identity_block
numpy.sum
keras.layers.Reshape
r.x.astype
numpy.zeros
sub_block_id.str.block_id.str.random_normal.ratio.hidden_channel._make_divisible.Conv2D
tmp_box.append
resnet50.ResNet50
numpy.argsort
self.ExponentDecayScheduler.super.__init__
keras.backend.sigmoid
os.path.abspath
_make_divisible
pycocotools.cocoeval.COCOeval.summarize
yolo.detect_image.save
kwargs.get
sorted
log_average_miss_rate
PIL.Image.open
format.Multiply
utils.utils.net_flops
keras.layers.add
print
utils.utils_map.get_coco_map
tensorflow.TensorArray
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nets.yolo.get_train_model.compile
keras.backend.floatx

    @developer Could please help me check this issue? May I pull a request to fix it? Thank you very much.

    opened by PyDeps 0
  • AttributeError: 'str' object has no attribute 'decode'

    AttributeError: 'str' object has no attribute 'decode'

    Traceback (most recent call last): File "D:/AI/projects/mobilenet-yolov4-lite-keras-main/mobilenet-yolov4-lite-keras-main/train.py", line 257, in model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) File "D:\AI\anaconda3-5.3.1\envs\yolov4\lib\site-packages\keras\engine\topology.py", line 2653, in load_weights reshape=reshape) File "D:\AI\anaconda3-5.3.1\envs\yolov4\lib\site-packages\keras\engine\topology.py", line 3407, in load_weights_from_hdf5_group_by_name original_keras_version = f.attrs['keras_version'].decode('utf8') AttributeError: 'str' object has no attribute 'decode' 训练时报错，怎能解决啊？

    opened by czalt1028 3
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