项目总结三：目标检测项目（Car detection with YOLOv2）
1、 the YOLO model （YOLO ，you only look once）
（1）We will use 5 anchor boxes. So you can think of the YOLO architecture as the following: IMAGE (m, 608, 608, 3) -> DEEP CNN -> ENCODING (m, 19, 19, 5, 85).
（2）Training a YOLO model takes a very long time and requires a fairly large dataset of labelled bounding boxes for a large range of target classes. We are going to load an existing pretrained Keras YOLO model stored in "yolo.h5". (These weights come from the official YOLO website, and were converted using a function written by Allan Zelener.)
（3）model summry (模型信息)
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 608, 608, 3) 0
____________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 608, 608, 32) 864 input_1[0][0]
____________________________________________________________________________________________________
batch_normalization_1 (BatchNorm (None, 608, 608, 32) 128 conv2d_1[0][0]
____________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 608, 608, 32) 0 batch_normalization_1[0][0]
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 304, 304, 32) 0 leaky_re_lu_1[0][0]
____________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 304, 304, 64) 18432 max_pooling2d_1[0][0]
____________________________________________________________________________________________________
batch_normalization_2 (BatchNorm (None, 304, 304, 64) 256 conv2d_2[0][0]
____________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 304, 304, 64) 0 batch_normalization_2[0][0]
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 152, 152, 64) 0 leaky_re_lu_2[0][0]
____________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 152, 152, 128) 73728 max_pooling2d_2[0][0]
____________________________________________________________________________________________________
batch_normalization_3 (BatchNorm (None, 152, 152, 128) 512 conv2d_3[0][0]
____________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 152, 152, 128) 0 batch_normalization_3[0][0]
____________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 152, 152, 64) 8192 leaky_re_lu_3[0][0]
____________________________________________________________________________________________________
batch_normalization_4 (BatchNorm (None, 152, 152, 64) 256 conv2d_4[0][0]
____________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, 152, 152, 64) 0 batch_normalization_4[0][0]
____________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 152, 152, 128) 73728 leaky_re_lu_4[0][0]
____________________________________________________________________________________________________
batch_normalization_5 (BatchNorm (None, 152, 152, 128) 512 conv2d_5[0][0]
____________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU) (None, 152, 152, 128) 0 batch_normalization_5[0][0]
____________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 76, 76, 128) 0 leaky_re_lu_5[0][0]
____________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 76, 76, 256) 294912 max_pooling2d_3[0][0]
____________________________________________________________________________________________________
batch_normalization_6 (BatchNorm (None, 76, 76, 256) 1024 conv2d_6[0][0]
____________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_6[0][0]
____________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 76, 76, 128) 32768 leaky_re_lu_6[0][0]
____________________________________________________________________________________________________
batch_normalization_7 (BatchNorm (None, 76, 76, 128) 512 conv2d_7[0][0]
____________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, 76, 76, 128) 0 batch_normalization_7[0][0]
____________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 76, 76, 256) 294912 leaky_re_lu_7[0][0]
____________________________________________________________________________________________________
batch_normalization_8 (BatchNorm (None, 76, 76, 256) 1024 conv2d_8[0][0]
____________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_8[0][0]
____________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 38, 38, 256) 0 leaky_re_lu_8[0][0]
____________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 38, 38, 512) 1179648 max_pooling2d_4[0][0]
____________________________________________________________________________________________________
batch_normalization_9 (BatchNorm (None, 38, 38, 512) 2048 conv2d_9[0][0]
____________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_9[0][0]
____________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_9[0][0]
____________________________________________________________________________________________________
batch_normalization_10 (BatchNor (None, 38, 38, 256) 1024 conv2d_10[0][0]
____________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_10[0][0]
____________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_10[0][0]
____________________________________________________________________________________________________
batch_normalization_11 (BatchNor (None, 38, 38, 512) 2048 conv2d_11[0][0]
____________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_11[0][0]
____________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_11[0][0]
____________________________________________________________________________________________________
batch_normalization_12 (BatchNor (None, 38, 38, 256) 1024 conv2d_12[0][0]
____________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_12[0][0]
____________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_12[0][0]
____________________________________________________________________________________________________
batch_normalization_13 (BatchNor (None, 38, 38, 512) 2048 conv2d_13[0][0]
____________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_13[0][0]
____________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 19, 19, 512) 0 leaky_re_lu_13[0][0]
____________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 19, 19, 1024) 4718592 max_pooling2d_5[0][0]
____________________________________________________________________________________________________
batch_normalization_14 (BatchNor (None, 19, 19, 1024) 4096 conv2d_14[0][0]
____________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_14[0][0]
____________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_14[0][0]
____________________________________________________________________________________________________
batch_normalization_15 (BatchNor (None, 19, 19, 512) 2048 conv2d_15[0][0]
____________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_15[0][0]
____________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_15[0][0]
____________________________________________________________________________________________________
batch_normalization_16 (BatchNor (None, 19, 19, 1024) 4096 conv2d_16[0][0]
____________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_16[0][0]
____________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_16[0][0]
____________________________________________________________________________________________________
batch_normalization_17 (BatchNor (None, 19, 19, 512) 2048 conv2d_17[0][0]
____________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_17[0][0]
____________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_17[0][0]
____________________________________________________________________________________________________
batch_normalization_18 (BatchNor (None, 19, 19, 1024) 4096 conv2d_18[0][0]
____________________________________________________________________________________________________
leaky_re_lu_18 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_18[0][0]
____________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_18[0][0]
____________________________________________________________________________________________________
batch_normalization_19 (BatchNor (None, 19, 19, 1024) 4096 conv2d_19[0][0]
____________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 38, 38, 64) 32768 leaky_re_lu_13[0][0]
____________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_19[0][0]
____________________________________________________________________________________________________
batch_normalization_21 (BatchNor (None, 38, 38, 64) 256 conv2d_21[0][0]
____________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_19[0][0]
____________________________________________________________________________________________________
leaky_re_lu_21 (LeakyReLU) (None, 38, 38, 64) 0 batch_normalization_21[0][0]
____________________________________________________________________________________________________
batch_normalization_20 (BatchNor (None, 19, 19, 1024) 4096 conv2d_20[0][0]
____________________________________________________________________________________________________
space_to_depth_x2 (Lambda) (None, 19, 19, 256) 0 leaky_re_lu_21[0][0]
____________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_20[0][0]
____________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 19, 19, 1280) 0 space_to_depth_x2[0][0]
leaky_re_lu_20[0][0]
____________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 19, 19, 1024) 11796480 concatenate_1[0][0]
____________________________________________________________________________________________________
batch_normalization_22 (BatchNor (None, 19, 19, 1024) 4096 conv2d_22[0][0]
____________________________________________________________________________________________________
leaky_re_lu_22 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_22[0][0]
____________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 19, 19, 425) 435625 leaky_re_lu_22[0][0]
====================================================================================================
Total params: 50,983,561
Trainable params: 50,962,889
Non-trainable params: 20,672
2、输入输出数据类型
（1）输入数据：(m, 608, 608, 3)
（2）输出数据：(m, 19, 19, 5, 85)
3、检测过程
（1）Score-thresholding：throw away boxes that have detected a class with a score less than the threshold（0.4）
（2）Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes
- Select the box that has the highest score.
- Compute its overlap with all other boxes, and remove boxes that overlap it more than
iou_threshold. - Go back to step 1 and iterate until there's no more boxes with a lower score than the current selected box.
This will remove all boxes that have a large overlap with the selected boxes. Only the "best" boxes remain.
4、总结
- YOLO is a state-of-the-art object detection model that is fast and accurate
- It runs an input image through a CNN which outputs a 19x19x5x85 dimensional volume.
- The encoding can be seen as a grid where each of the 19x19 cells contains information about 5 boxes.
- You filter through all the boxes using non-max suppression. Specifically:
- Score thresholding on the probability of detecting a class to keep only accurate (high probability) boxes
- Intersection over Union (IoU) thresholding to eliminate overlapping boxes
- Because training a YOLO model from randomly initialized weights is non-trivial and requires a large dataset as well as lot of computation, we used previously trained model parameters in this exercise. If you wish, you can also try fine-tuning the YOLO model with your own dataset, though this would be a fairly non-trivial exercise.
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