dlib自带demo 基于DNN的车辆检测模型训练（转）
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2022-03-06 14:30:21
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原文：https://blog.csdn.net/longji/article/details/78069213
01 资源
代码:dlib\examples\dnn_mmod_train_find_cars_ex.cpp
工程名:dnn_mmod_train_find_cars_ex
从代码注释中可以获得model数据文件：
http://dlib.net/files/data/dlib_rear_end_vehicles_v1.tar
把上面获得的压缩包内容分别解压到D:\git\dlib\data\dlib_rear_end_vehicles目录下：
D:\git\dlib\data\dlib_rear_end_vehicles\normal_rgb_images
D:\git\dlib\data\dlib_rear_end_vehicles\youtube_frames
D:\git\dlib\data\dlib_rear_end_vehicles\image_metadata_stylesheet.xsl
D:\git\dlib\data\dlib_rear_end_vehicles\README.txt
D:\git\dlib\data\dlib_rear_end_vehicles\testing.xml
D:\git\dlib\data\dlib_rear_end_vehicles\training.xml
02 项目设置
把examples解决方案中的dnn_mmod_train_find_cars_ex工程设置为启动项。
如需调试，使用debug。使用release运行速度会快很多。
配置属性==>调试==>命令参数==>..\..\..\data\dlib_rear_end_vehicles
配置属性==>调试==>工作目录==>$(OutDir)
03 运行结果
使用Release版本dnn_mmod_train_find_cars_ex.exe运行40+小时，训练数据收敛较慢。
D:\git\dlib\build\x64_19.6_examples\Release>dnn_mmod_train_find_cars_ex.exe ..\..\..\data\dlib_rear_end_vehicles
num_overlapped_ignored: 8
num_additional_ignored: 907
num_overlapped_ignored_test: 2
num training images: 2217
num testing images: 135
dnn_trainer details:
net_type::num_layers: 21
net size: 0.00187111MB
net architecture hash: c49cec4591d03ef2fae98d750db474d4
loss: loss_mmod (detector_windows:(70x56,50x70,70x34), loss per FA:1, loss per miss:1, truth match IOU thresh:0.5, overlaps_nms:(0.521561,0.981482), overlaps_ignore:(0.5,0.95))
synchronization file: mmod_cars_sync
trainer.get_solvers()[0]: sgd: weight_decay=0.0001, momentum=0.9
learning rate: 0.1
learning rate shrink factor: 0.1
min learning rate: 1e-05
iterations without progress threshold: 50000
test iterations without progress threshold: 1000
random_cropper details:
chip_dims.rows: 350
chip_dims.cols: 350
randomly_flip: true
max_rotation_degrees: 2
min_object_size: 0.2
max_object_size: 0.7
background_crops_fraction: 0.5
translate_amount: 0.1
step#: 0 learning rate: 0.1 average loss: 0 steps without apparent progress: 0
step#: 2 learning rate: 0.1 average loss: 214.984 steps without apparent progress: 0
Saved state to mmod_cars_sync
step#: 3 learning rate: 0.1 average loss: 181.586 steps without apparent progress: 0
Saved state to mmod_cars_sync_
step#: 4 learning rate: 0.1 average loss: 157.216 steps without apparent progress: 0
Saved state to mmod_cars_sync
step#: 5 learning rate: 0.1 average loss: 139.205 steps without apparent progress: 0
Saved state to mmod_cars_sync_
step#: 6 learning rate: 0.1 average loss: 114.955 steps without apparent progress: 0
Saved state to mmod_cars_sync
step#: 7 learning rate: 0.1 average loss: 100.031 steps without apparent progress: 0
Saved state to mmod_cars_sync_
step#: 8 learning rate: 0.1 average loss: 79.5851 steps without apparent progress: 0
Saved state to mmod_cars_sync
...
step#: 155 learning rate: 0.1 train loss: 4.00966 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 156 learning rate: 0.1 train loss: 5.08666 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync
step#: 157 learning rate: 0.1 train loss: 4.59453 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 158 learning rate: 0.1 train loss: 3.75625 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync
step#: 159 learning rate: 0.1 train loss: 4.30563 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 160 learning rate: 0.1 train loss: 3.82061 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync
step#: 161 learning rate: 0.1 train loss: 3.96813 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 162 learning rate: 0.1 train loss: 4.37916 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync
step#: 163 learning rate: 0.1 train loss: 5.28656 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 164 learning rate: 0.1 train loss: 4.55476 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync
step#: 165 learning rate: 0.1 train loss: 4.85363 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 166 learning rate: 0.1 train loss: 4.53134 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync
step#: 167 learning rate: 0.1 train loss: 4.40066 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 168 learning rate: 0.1 train loss: 4.18543 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync
step#: 169 learning rate: 0.1 train loss: 3.61347 test loss: 5.36686 steps without apparent progress: train=0, test=0
Saved state to mmod_cars_sync_
step#: 170 learning rate: 0.104 代码
dlib\examples\dnn_mmod_train_find_cars_ex.cpp
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This example shows how to train a CNN based object detector using dlib's
loss_mmod loss layer. This loss layer implements the Max-Margin Object
Detection loss as described in the paper:
Max-Margin Object Detection by Davis E. King (http://arxiv.org/abs/1502.00046).
This is the same loss used by the popular SVM+HOG object detector in dlib
(see fhog_object_detector_ex.cpp) except here we replace the HOG features
with a CNN and train the entire detector end-to-end. This allows us to make
much more powerful detectors.
It would be a good idea to become familiar with dlib's DNN tooling before reading this
example. So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
before reading this example program. You should also read the introductory DNN+MMOD
example dnn_mmod_ex.cpp as well before proceeding.
This example is essentially a more complex version of dnn_mmod_ex.cpp. In it we train
a detector that finds the rear ends of motor vehicles. I will also discuss some
aspects of data preparation useful when training this kind of detector.
*/
#include <iostream>
#include <dlib/dnn.h>
#include <dlib/data_io.h>
using namespace std;
using namespace dlib;
template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
template <long num_filters, typename SUBNET> using con5 = con<num_filters,5,5,1,1,SUBNET>;
template <typename SUBNET> using downsampler = relu<bn_con<con5d<32, relu<bn_con<con5d<32, relu<bn_con<con5d<16,SUBNET>>>>>>>>>;
template <typename SUBNET> using rcon5 = relu<bn_con<con5<55,SUBNET>>>;
using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
// ----------------------------------------------------------------------------------------
int ignore_overlapped_boxes(
std::vector<mmod_rect>& boxes,
const test_box_overlap& overlaps
)
/*!
ensures
- Whenever two rectangles in boxes overlap, according to overlaps(), we set the
smallest box to ignore.
- returns the number of newly ignored boxes.
!*/
{
int num_ignored = 0;
for (size_t i = 0; i < boxes.size(); ++i)
{
if (boxes[i].ignore)
continue;
for (size_t j = i+1; j < boxes.size(); ++j)
{
if (boxes[j].ignore)
continue;
if (overlaps(boxes[i], boxes[j]))
{
++num_ignored;
if(boxes[i].rect.area() < boxes[j].rect.area())
boxes[i].ignore = true;
else
boxes[j].ignore = true;
}
}
}
return num_ignored;
}
// ----------------------------------------------------------------------------------------
int main(int argc, char** argv) try
{
if (argc != 2)
{
cout << "Give the path to a folder containing training.xml and testing.xml files." << endl;
cout << "This example program is specifically designed to run on the dlib vehicle " << endl;
cout << "detection dataset, which is available at this URL: " << endl;
cout << " http://dlib.net/files/data/dlib_rear_end_vehicles_v1.tar" << endl;
cout << endl;
cout << "So download that dataset, extract it somewhere, and then run this program" << endl;
cout << "with the dlib_rear_end_vehicles folder as an argument. E.g. if you extract" << endl;
cout << "the dataset to the current folder then you should run this example program" << endl;
cout << "by typing: " << endl;
cout << " ./dnn_mmod_train_find_cars_ex dlib_rear_end_vehicles" << endl;
cout << endl;
cout << "It takes about a day to finish if run on a high end GPU like a 1080ti." << endl;
cout << endl;
return 0;
}
const std::string data_directory = argv[1];
std::vector<matrix<rgb_pixel>> images_train, images_test;
std::vector<std::vector<mmod_rect>> boxes_train, boxes_test;
load_image_dataset(images_train, boxes_train, data_directory+"/training.xml");
load_image_dataset(images_test, boxes_test, data_directory+"/testing.xml");
// When I was creating the dlib vehicle detection dataset I had to label all the cars
// in each image. MMOD requires all cars to be labeled, since any unlabeled part of an
// image is implicitly assumed to be not a car, and the algorithm will use it as
// negative training data. So every car must be labeled, either with a normal
// rectangle or an "ignore" rectangle that tells MMOD to simply ignore it (i.e. neither
// treat it as a thing to detect nor as negative training data).
//
// In our present case, many images contain very tiny cars in the distance, ones that
// are essentially just dark smudges. It's not reasonable to expect the CNN
// architecture we defined to detect such vehicles. However, I erred on the side of
// having more complete annotations when creating the dataset. So when I labeled these
// images I labeled many of these really difficult cases as vehicles to detect.
//
// So the first thing we are going to do is clean up our dataset a little bit. In
// particular, we are going to mark boxes smaller than 35*35 pixels as ignore since
// only really small and blurry cars appear at those sizes. We will also mark boxes
// that are heavily overlapped by another box as ignore. We do this because we want to
// allow for stronger non-maximum suppression logic in the learned detector, since that
// will help make it easier to learn a good detector.
//
// To explain this non-max suppression idea further it's important to understand how
// the detector works. Essentially, sliding window detectors scan all image locations
// and ask "is there a care here?". If there really is a car in a specific location in
// an image then usually many slightly different sliding window locations will produce
// high detection scores, indicating that there is a car at those locations. If we
// just stopped there then each car would produce multiple detections. But that isn't
// what we want. We want each car to produce just one detection. So it's common for
// detectors to include "non-maximum suppression" logic which simply takes the
// strongest detection and then deletes all detections "close to" the strongest. This
// is a simple post-processing step that can eliminate duplicate detections. However,
// we have to define what "close to" means. We can do this by looking at your training
// data and checking how close the closest target boxes are to each other, and then
// picking a "close to" measure that doesn't suppress those target boxes but is
// otherwise as tight as possible. This is exactly what the mmod_options object does
// by default.
//
// Importantly, this means that if your training dataset contains an image with two
// target boxes that really overlap a whole lot, then the non-maximum suppression
// "close to" measure will be configured to allow detections to really overlap a whole
// lot. On the other hand, if your dataset didn't contain any overlapped boxes at all,
// then the non-max suppression logic would be configured to filter out any boxes that
// overlapped at all, and thus would be performing a much stronger non-max suppression.
//
// Why does this matter? Well, remember that we want to avoid duplicate detections.
// If non-max suppression just kills everything in a really wide area around a car then
// the CNN doesn't really need to learn anything about avoiding duplicate detections.
// However, if non-max suppression only suppresses a tiny area around each detection
// then the CNN will need to learn to output small detection scores for those areas of
// the image not suppressed. The smaller the non-max suppression region the more the
// CNN has to learn and the more difficult the learning problem will become. This is
// why we remove highly overlapped objects from the training dataset. That is, we do
// it so the non-max suppression logic will be able to be reasonably effective. Here
// we are ensuring that any boxes that are entirely contained by another are
// suppressed. We also ensure that boxes with an intersection over union of 0.5 or
// greater are suppressed. This will improve the resulting detector since it will be
// able to use more aggressive non-max suppression settings.
int num_overlapped_ignored_test = 0;
for (auto& v : boxes_test)
num_overlapped_ignored_test += ignore_overlapped_boxes(v, test_box_overlap(0.50, 0.99));
int num_overlapped_ignored = 0;
int num_additional_ignored = 0;
for (auto& v : boxes_train)
{
num_overlapped_ignored += ignore_overlapped_boxes(v, test_box_overlap(0.50, 0.99));
for (auto& bb : v)
{
if (bb.rect.width() < 35 && bb.rect.height() < 35)
{
if (!bb.ignore)
{
bb.ignore = true;
++num_additional_ignored;
}
}
// The dlib vehicle detection dataset doesn't contain any detections with
// really extreme aspect ratios. However, some datasets do, often because of
// bad labeling. So it's a good idea to check for that and either eliminate
// those boxes or set them to ignore. Although, this depends on your
// application.
//
// For instance, if your dataset has boxes with an aspect ratio
// of 10 then you should think about what that means for the network
// architecture. Does the receptive field even cover the entirety of the box
// in those cases? Do you care about these boxes? Are they labeling errors?
// I find that many people will download some dataset from the internet and
// just take it as given. They run it through some training algorithm and take
// the dataset as unchallengeable truth. But many datasets are full of
// labeling errors. There are also a lot of datasets that aren't full of
// errors, but are annotated in a sloppy and inconsistent way. Fixing those
// errors and inconsistencies can often greatly improve models trained from
// such data. It's almost always worth the time to try and improve your
// training dataset.
//
// In any case, my point is that there are other types of dataset cleaning you
// could put here. What exactly you need depends on your application. But you
// should carefully consider it and not take your dataset as a given. The work
// of creating a good detector is largely about creating a high quality
// training dataset.
}
}
// When modifying a dataset like this, it's a really good idea to print a log of how
// many boxes you ignored. It's easy to accidentally ignore a huge block of data, so
// you should always look and see that things are doing what you expect.
cout << "num_overlapped_ignored: "<< num_overlapped_ignored << endl;
cout << "num_additional_ignored: "<< num_additional_ignored << endl;
cout << "num_overlapped_ignored_test: "<< num_overlapped_ignored_test << endl;
cout << "num training images: " << images_train.size() << endl;
cout << "num testing images: " << images_test.size() << endl;
// Our vehicle detection dataset has basically 3 different types of boxes. Square
// boxes, tall and skinny boxes (e.g. semi trucks), and short and wide boxes (e.g.
// sedans). Here we are telling the MMOD algorithm that a vehicle is recognizable as
// long as the longest box side is at least 70 pixels long and the shortest box side is
// at least 30 pixels long. mmod_options will use these parameters to decide how large
// each of the sliding windows needs to be so as to be able to detect all the vehicles.
// Since our dataset has basically these 3 different aspect ratios, it will decide to
// use 3 different sliding windows. This means the final con layer in the network will
// have 3 filters, one for each of these aspect ratios.
//
// Another thing to consider when setting the sliding window size is the "stride" of
// your network. The network we defined above downsamples the image by a factor of 8x
// in the first few layers. So when the sliding windows are scanning the image, they
// are stepping over it with a stride of 8 pixels. If you set the sliding window size
// too small then the stride will become an issue. For instance, if you set the
// sliding window size to 4 pixels, then it means a 4x4 window will be moved by 8
// pixels at a time when scanning. This is obviously a problem since 75% of the image
// won't even be visited by the sliding window. So you need to set the window size to
// be big enough relative to the stride of your network. In our case, the windows are
// at least 30 pixels in length, so being moved by 8 pixel steps is fine.
mmod_options options(boxes_train, 70, 30);
// This setting is very important and dataset specific. The vehicle detection dataset
// contains boxes that are marked as "ignore", as we discussed above. Some of them are
// ignored because we set ignore to true in the above code. However, the xml files
// also contained a lot of ignore boxes. Some of them are large boxes that encompass
// large parts of an image and the intention is to have everything inside those boxes
// be ignored. Therefore, we need to tell the MMOD algorithm to do that, which we do
// by setting options.overlaps_ignore appropriately.
//
// But first, we need to understand exactly what this option does. The MMOD loss
// is essentially counting the number of false alarms + missed detections produced by
// the detector for each image. During training, the code is running the detector on
// each image in a mini-batch and looking at its output and counting the number of
// mistakes. The optimizer tries to find parameters settings that minimize the number
// of detector mistakes.
//
// This overlaps_ignore option allows you to tell the loss that some outputs from the
// detector should be totally ignored, as if they never happened. In particular, if a
// detection overlaps a box in the training data with ignore==true then that detection
// is ignored. This overlap is determined by calling
// options.overlaps_ignore(the_detection, the_ignored_training_box). If it returns
// true then that detection is ignored.
//
// You should read the documentation for test_box_overlap, the class type for
// overlaps_ignore for full details. However, the gist is that the default behavior is
// to only consider boxes as overlapping if their intersection over union is > 0.5.
// However, the dlib vehicle detection dataset contains large boxes that are meant to
// mask out large areas of an image. So intersection over union isn't an appropriate
// way to measure "overlaps with box" in this case. We want any box that is contained
// inside one of these big regions to be ignored, even if the detection box is really
// small. So we set overlaps_ignore to behave that way with this line.
options.overlaps_ignore = test_box_overlap(0.5, 0.95);
net_type net(options);
// The final layer of the network must be a con layer that contains
// options.detector_windows.size() filters. This is because these final filters are
// what perform the final "sliding window" detection in the network. For the dlib
// vehicle dataset, there will be 3 sliding window detectors, so we will be setting
// num_filters to 3 here.
net.subnet().layer_details().set_num_filters(options.detector_windows.size());
dnn_trainer<net_type> trainer(net,sgd(0.0001,0.9));
trainer.set_learning_rate(0.1);
trainer.be_verbose();
// While training, we are going to use early stopping. That is, we will be checking
// how good the detector is performing on our test data and when it stops getting
// better on the test data we will drop the learning rate. We will keep doing that
// until the learning rate is less than 1e-4. These two settings tell the trainer to
// do that. Essentially, we are setting the first argument to infinity, and only the
// test iterations without progress threshold will matter. In particular, it says that
// once we observe 1000 testing mini-batches where the test loss clearly isn't
// decreasing we will lower the learning rate.
trainer.set_iterations_without_progress_threshold(50000);
trainer.set_test_iterations_without_progress_threshold(1000);
const string sync_filename = "mmod_cars_sync";
trainer.set_synchronization_file(sync_filename, std::chrono::minutes(5));
std::vector<matrix<rgb_pixel>> mini_batch_samples;
std::vector<std::vector<mmod_rect>> mini_batch_labels;
random_cropper cropper;
cropper.set_seed(1);
cropper.set_chip_dims(350, 350);
cropper.set_min_object_size(0.20);
cropper.set_max_rotation_degrees(2);
dlib::rand rnd;
// Log the training parameters to the console
cout << trainer << cropper << endl;
int cnt = 1;
// Run the trainer until the learning rate gets small.
while(trainer.get_learning_rate() >= 1e-4)
{
// Every 30 mini-batches we do a testing mini-batch.
if (cnt%30 != 0 || images_test.size() == 0)
{
cropper(87, images_train, boxes_train, mini_batch_samples, mini_batch_labels);
// We can also randomly jitter the colors and that often helps a detector
// generalize better to new images.
for (auto&& img : mini_batch_samples)
disturb_colors(img, rnd);
// It's a good idea to, at least once, put code here that displays the images
// and boxes the random cropper is generating. You should look at them and
// think about if the output makes sense for your problem. Most of the time
// it will be fine, but sometimes you will realize that the pattern of cropping
// isn't really appropriate for your problem and you will need to make some
// change to how the mini-batches are being generated. Maybe you will tweak
// some of the cropper's settings, or write your own entirely separate code to
// create mini-batches. But either way, if you don't look you will never know.
// An easy way to do this is to create a dlib::image_window to display the
// images and boxes.
trainer.train_one_step(mini_batch_samples, mini_batch_labels);
}
else
{
cropper(87, images_test, boxes_test, mini_batch_samples, mini_batch_labels);
// We can also randomly jitter the colors and that often helps a detector
// generalize better to new images.
for (auto&& img : mini_batch_samples)
disturb_colors(img, rnd);
trainer.test_one_step(mini_batch_samples, mini_batch_labels);
}
++cnt;
}
// wait for training threads to stop
trainer.get_net();
cout << "done training" << endl;
// Save the network to disk
net.clean();
serialize("mmod_rear_end_vehicle_detector.dat") << net;
// It's a really good idea to print the training parameters. This is because you will
// invariably be running multiple rounds of training and should be logging the output
// to a file. This print statement will include many of the training parameters in
// your log.
cout << trainer << cropper << endl;
cout << "\nsync_filename: " << sync_filename << endl;
cout << "num training images: "<< images_train.size() << endl;
cout << "training results: " << test_object_detection_function(net, images_train, boxes_train, test_box_overlap(), 0, options.overlaps_ignore);
// Upsampling the data will allow the detector to find smaller cars. Recall that
// we configured it to use a sliding window nominally 70 pixels in size. So upsampling
// here will let it find things nominally 35 pixels in size. Although we include a
// limit of 1800*1800 here which means "don't upsample an image if it's already larger
// than 1800*1800". We do this so we don't run out of RAM, which is a concern because
// some of the images in the dlib vehicle dataset are really high resolution.
upsample_image_dataset<pyramid_down<2>>(images_train, boxes_train, 1800*1800);
cout << "training upsampled results: " << test_object_detection_function(net, images_train, boxes_train, test_box_overlap(), 0, options.overlaps_ignore);
cout << "num testing images: "<< images_test.size() << endl;
cout << "testing results: " << test_object_detection_function(net, images_test, boxes_test, test_box_overlap(), 0, options.overlaps_ignore);
upsample_image_dataset<pyramid_down<2>>(images_test, boxes_test, 1800*1800);
cout << "testing upsampled results: " << test_object_detection_function(net, images_test, boxes_test, test_box_overlap(), 0, options.overlaps_ignore);
/*
This program takes many hours to execute on a high end GPU. It took about a day to
train on a NVIDIA 1080ti. The resulting model file is available at
http://dlib.net/files/mmod_rear_end_vehicle_detector.dat.bz2
It should be noted that this file on dlib.net has a dlib::shape_predictor appended
onto the end of it (see dnn_mmod_find_cars_ex.cpp for an example of its use). This
explains why the model file on dlib.net is larger than the
mmod_rear_end_vehicle_detector.dat output by this program.
You can see some videos of this vehicle detector running on YouTube:
https://www.youtube.com/watch?v=4B3bzmxMAZU
https://www.youtube.com/watch?v=bP2SUo5vSlc
Also, the training and testing accuracies were:
num training images: 2217
training results: 0.990738 0.736431 0.736073
training upsampled results: 0.986837 0.937694 0.936912
num testing images: 135
testing results: 0.988827 0.471372 0.470806
testing upsampled results: 0.987879 0.651132 0.650399
*/
return 0;
}
catch(std::exception& e)
{
cout << e.what() << endl;
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