狗狗识别-百度西安交通大学大数据比赛baseline=0.2代码

题目简单介绍

官方提供100类狗的图片，选手将狗进行分类。

分析

官方提供的图片如下：


从图片可以看出背景复杂，图片中不仅有狗还有人，可能还有其他物体的背景，因此第一步需要将狗提取出来。考虑到从先训练一个狗的检测器将耗费一定的时间，因此采用目标检测SSD方法进行狗的检测。将狗检测出来再进行分类。
因此流程就是 检测 + 分类。

狗的检测核心代码

该代码使用Qt编写。项目配置如下：

INCLUDEPATH += /home/young/deeplearning/SSD/caffe/include \            += /usr/include \            += /home/young/deeplearning/SSD/caffe/src \            += /home/young/software/cuda/includeLIBS += -L/home/young/deeplearning/SSD/caffe/build/lib -lcaffeLIBS += -L/usr/lib/x86_64-linux-gnu -lopencv_core -lopencv_imgproc -lopencv_highguiLIBS += -lglog -lgflags -lprotobuf -lboost_system -lboost_thread  -latlasSOURCES += \    main.cpp# cudaINCLUDEPATH += /usr/local/cuda/includeLIBS += -L/usr/local/cuda/lib64 -lcudart -lcublas -lcurand#cudnnLIBS += -L/home/young/software/cuda/lib64 -lcudnn

SSD检测代码

#include "caffe/caffe.hpp"#define USE_OPENCV#define CPU_ONLY#ifdef USE_OPENCV#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#endif  // USE_OPENCV#include <algorithm>#include <iomanip>#include <iosfwd>#include <memory>#include <string>#include <utility>#include <vector>#include<fstream>#ifdef USE_OPENCVusing namespace caffe;  // NOLINT(build/namespaces)std::vector<std::string> splitEx(const std::string& src, std::string separate_character){   std::vector<std::string> strs;  int separate_characterLen = separate_character.size();//分割字符串的长度,这样就可以支持如“,,”多字符串的分隔符    int lastPosition = 0,index = -1;    while (-1 != (index = src.find(separate_character,lastPosition)))    {        strs.push_back(src.substr(lastPosition,index - lastPosition));        lastPosition = index + separate_characterLen;    }    std::string lastString = src.substr(lastPosition);//截取最后一个分隔符后的内容    if (!lastString.empty())        strs.push_back(lastString);//如果最后一个分隔符后还有内容就入队    return strs;}class Detector { public:  Detector(const string& model_file,           const string& weights_file,           const string& mean_file,           const string& mean_value);  std::vector<vector<float> > Detect(const cv::Mat& img); private:  void SetMean(const string& mean_file, const string& mean_value);  void WrapInputLayer(std::vector<cv::Mat>* input_channels);  void Preprocess(const cv::Mat& img,                  std::vector<cv::Mat>* input_channels); private:  shared_ptr<Net<float> > net_;  cv::Size input_geometry_;  int num_channels_;  cv::Mat mean_;};Detector::Detector(const string& model_file,                   const string& weights_file,                   const string& mean_file,                   const string& mean_value) {#ifdef CPU_ONLY  Caffe::set_mode(Caffe::CPU);#else  Caffe::set_mode(Caffe::GPU);#endif  /* Load the network. */  net_.reset(new Net<float>(model_file, TEST));  net_->CopyTrainedLayersFrom(weights_file);  CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";  CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";  Blob<float>* input_layer = net_->input_blobs()[0];  num_channels_ = input_layer->channels();  CHECK(num_channels_ == 3 || num_channels_ == 1)    << "Input layer should have 1 or 3 channels.";  input_geometry_ = cv::Size(input_layer->width(), input_layer->height());  /* Load the binaryproto mean file. */  SetMean(mean_file, mean_value);}std::vector<vector<float> > Detector::Detect(const cv::Mat& img) {  Blob<float>* input_layer = net_->input_blobs()[0];  input_layer->Reshape(1, num_channels_,                       input_geometry_.height, input_geometry_.width);  /* Forward dimension change to all layers. */  net_->Reshape();  std::vector<cv::Mat> input_channels;  WrapInputLayer(&input_channels);  Preprocess(img, &input_channels);  net_->Forward();  /* Copy the output layer to a std::vector */  Blob<float>* result_blob = net_->output_blobs()[0];  const float* result = result_blob->cpu_data();  const int num_det = result_blob->height();  vector<vector<float> > detections;  for (int k = 0; k < num_det; ++k) {    if (result[0] == -1) {      // Skip invalid detection.      result += 7;      continue;    }    vector<float> detection(result, result + 7);    detections.push_back(detection);    result += 7;  }  return detections;}/* Load the mean file in binaryproto format. */void Detector::SetMean(const string& mean_file, const string& mean_value) {  cv::Scalar channel_mean;  if (!mean_file.empty()) {    CHECK(mean_value.empty()) <<      "Cannot specify mean_file and mean_value at the same time";    BlobProto blob_proto;    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);    /* Convert from BlobProto to Blob<float> */    Blob<float> mean_blob;    mean_blob.FromProto(blob_proto);    CHECK_EQ(mean_blob.channels(), num_channels_)      << "Number of channels of mean file doesn't match input layer.";    /* The format of the mean file is planar 32-bit float BGR or grayscale. */    std::vector<cv::Mat> channels;    float* data = mean_blob.mutable_cpu_data();    for (int i = 0; i < num_channels_; ++i) {      /* Extract an individual channel. */      cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);      channels.push_back(channel);      data += mean_blob.height() * mean_blob.width();    }    /* Merge the separate channels into a single image. */    cv::Mat mean;    cv::merge(channels, mean);    /* Compute the global mean pixel value and create a mean image     * filled with this value. */    channel_mean = cv::mean(mean);    mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);  }  if (!mean_value.empty()) {    CHECK(mean_file.empty()) <<      "Cannot specify mean_file and mean_value at the same time";    stringstream ss(mean_value);    vector<float> values;    string item;    while (getline(ss, item, ',')) {      float value = std::atof(item.c_str());      values.push_back(value);    }    CHECK(values.size() == 1 || values.size() == num_channels_) <<      "Specify either 1 mean_value or as many as channels: " << num_channels_;    std::vector<cv::Mat> channels;    for (int i = 0; i < num_channels_; ++i) {      /* Extract an individual channel. */      cv::Mat channel(input_geometry_.height, input_geometry_.width, CV_32FC1,          cv::Scalar(values[i]));      channels.push_back(channel);    }    cv::merge(channels, mean_);  }}/* Wrap the input layer of the network in separate cv::Mat objects * (one per channel). This way we save one memcpy operation and we * don't need to rely on cudaMemcpy2D. The last preprocessing * operation will write the separate channels directly to the input * layer. */void Detector::WrapInputLayer(std::vector<cv::Mat>* input_channels) {  Blob<float>* input_layer = net_->input_blobs()[0];  int width = input_layer->width();  int height = input_layer->height();  float* input_data = input_layer->mutable_cpu_data();  for (int i = 0; i < input_layer->channels(); ++i) {    cv::Mat channel(height, width, CV_32FC1, input_data);    input_channels->push_back(channel);    input_data += width * height;  }}void Detector::Preprocess(const cv::Mat& img,                            std::vector<cv::Mat>* input_channels) {  /* Convert the input image to the input image format of the network. */  cv::Mat sample;  if (img.channels() == 3 && num_channels_ == 1)    cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);  else if (img.channels() == 4 && num_channels_ == 1)    cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);  else if (img.channels() == 4 && num_channels_ == 3)    cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);  else if (img.channels() == 1 && num_channels_ == 3)    cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);  else    sample = img;  cv::Mat sample_resized;  if (sample.size() != input_geometry_)    cv::resize(sample, sample_resized, input_geometry_);  else    sample_resized = sample;  cv::Mat sample_float;  if (num_channels_ == 3)    sample_resized.convertTo(sample_float, CV_32FC3);  else    sample_resized.convertTo(sample_float, CV_32FC1);  cv::Mat sample_normalized;  cv::subtract(sample_float, mean_, sample_normalized);  /* This operation will write the separate BGR planes directly to the   * input layer of the network because it is wrapped by the cv::Mat   * objects in input_channels. */  cv::split(sample_normalized, *input_channels);  CHECK(reinterpret_cast<float*>(input_channels->at(0).data)        == net_->input_blobs()[0]->cpu_data())    << "Input channels are not wrapping the input layer of the network.";}DEFINE_string(mean_file, "",    "The mean file used to subtract from the input image.");DEFINE_string(mean_value, "104,117,123",    "If specified, can be one value or can be same as image channels"    " - would subtract from the corresponding channel). Separated by ','."    "Either mean_file or mean_value should be provided, not both.");DEFINE_string(file_type, "image",    "The file type in the list_file. Currently support image and video.");DEFINE_string(out_file, "",    "If provided, store the detection results in the out_file.");DEFINE_double(confidence_threshold, 0.7,    "Only store detections with score higher than the threshold.");int main(int argc, char** argv) {  ::google::InitGoogleLogging(argv[0]);  // Print output to stderr (while still logging)  FLAGS_alsologtostderr = 1;#ifndef GFLAGS_GFLAGS_H_  namespace gflags = google;#endif  gflags::SetUsageMessage("Do detection using SSD mode.\n"        "Usage:\n"        "    ssd_detect [FLAGS] model_file weights_file list_file\n");  gflags::ParseCommandLineFlags(&argc, &argv, true);  /*  if (argc < 4) {    gflags::ShowUsageWithFlagsRestrict(argv[0], "examples/ssd/ssd_detect");    return 1;  }*/  const string& model_file = "./model/deploy.prototxt";  const string& weights_file = "./model/VGG_VOC0712Plus_SSD_300x300_iter_240000.caffemodel";  const string& mean_file = FLAGS_mean_file;  const string& mean_value = FLAGS_mean_value;  const string& file_type = FLAGS_file_type;  const string& out_file = FLAGS_out_file;  const float confidence_threshold = FLAGS_confidence_threshold;  // Initialize the network.  Detector detector(model_file, weights_file, mean_file, mean_value);  // Set the output mode.  std::streambuf* buf = std::cout.rdbuf();  std::ofstream outfile;  if (!out_file.empty()) {    outfile.open(out_file.c_str());    if (outfile.good()) {      buf = outfile.rdbuf();    }  }  std::ostream out(buf);  // Process image one by one.  std::ifstream infile("./img/val.txt");  std::string file;  std::ofstream outTrainfile;  //outTrainfile.open("./train.txt");  while (std::getline(infile, file)) {      outTrainfile.open("./val.txt", std::ios::app);    std::vector<std::string> line = splitEx(file, " ");    std::string imgPath = line[0];    std::string label = line[1].substr(0,line[1].find("\r"));    std::string prefixImg = splitEx(imgPath, ".")[0];    std::string postfixImg = splitEx(imgPath, ".")[1];    if (file_type == "image") {      cv::Mat img = cv::imread("./img/Img/" + imgPath, -1);      CHECK(!img.empty()) << "Unable to decode image " << file;      std::vector<vector<float> > detections = detector.Detect(img);      /* Print the detection results. */      int index = 0;      for (int i = 0; i < detections.size(); ++i) {        const vector<float>& d = detections[i];        // Detection format: [image_id, label, score, xmin, ymin, xmax, ymax].        CHECK_EQ(d.size(), 7);        const float score = d[2];        if (score >= confidence_threshold && static_cast<int>(d[1]) == 12) {          out << imgPath << " ";          out << label << " ";          out << static_cast<int>(d[1]) << " ";          out << score << " ";          out << static_cast<int>(d[3] * img.cols) << " ";          out << static_cast<int>(d[4] * img.rows) << " ";          out << static_cast<int>(d[5] * img.cols) << " ";          out << static_cast<int>(d[6] * img.rows) << std::endl;          int xmin = static_cast<int>(d[3] * img.cols);          int ymin = static_cast<int>(d[4] * img.rows);          int w  =  static_cast<int>(d[5] * img.cols);          int h = static_cast<int>(d[6] * img.rows);          if(xmin < 0) xmin = 0;          if(ymin < 0) ymin = 0;          if(xmin + w > img.cols) w = img.cols - xmin;          if(ymin + h > img.rows) h = img.rows - ymin;          cv::Mat saveImg(img, cv::Rect(xmin, ymin, w, h));          std::ostringstream stream;          stream<<index;          std::string newImgPath = prefixImg + "_" + stream.str() + "."+postfixImg;          //outTrainfile<< newImgPath << " " << label << "\n";          std::string savePath = "./val/" + label  + "/" + newImgPath;          outTrainfile << savePath << " " << label << "\n";          cv::imwrite(savePath, saveImg);          index++;          outTrainfile.close();        }      }    } else if (file_type == "video") {      cv::VideoCapture cap(file);      if (!cap.isOpened()) {        LOG(FATAL) << "Failed to open video: " << file;      }      cv::Mat img;      int frame_count = 0;      while (true) {        bool success = cap.read(img);        if (!success) {          LOG(INFO) << "Process " << frame_count << " frames from " << file;          break;        }        CHECK(!img.empty()) << "Error when read frame";        std::vector<vector<float> > detections = detector.Detect(img);        /* Print the detection results. */        for (int i = 0; i < detections.size(); ++i) {          const vector<float>& d = detections[i];          // Detection format: [image_id, label, score, xmin, ymin, xmax, ymax].          CHECK_EQ(d.size(), 7);          const float score = d[2];          if (score >= confidence_threshold) {              /*            out << file << "_";            out << std::setfill('0') << std::setw(6) << frame_count << " ";            out << static_cast<int>(d[1]) << " ";            out << score << " ";            out << static_cast<int>(d[3] * img.cols) << " ";            out << static_cast<int>(d[4] * img.rows) << " ";            out << static_cast<int>(d[5] * img.cols) << " ";            out << static_cast<int>(d[6] * img.rows) << std::endl;*/          }        }        ++frame_count;      }      if (cap.isOpened()) {        cap.release();      }    } else {      LOG(FATAL) << "Unknown file_type: " << file_type;    }  }  outTrainfile.close();  return 0;}#elseint main(int argc, char** argv) {  LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";}#endif  // USE_OPENCV

分类器选择

随着深度学习的发展，涌现出一些效果不错的网络，有AlexNet, VGG,ResNet,google系列的V1,V2,V3,V4。从ImageNet结果可以看出网络越深,分类效果越好。而且这些网络都公开了训练好的模型，由于比赛训练数据有限，采用迁移学习的思想进行网络学习。本人实验过VGG, ResNet18，V3，V3的网络效果最好。
训练代码如下：

from keras.applications.inception_v3 import InceptionV3import osfrom keras.layers import Flatten, Dense, AveragePooling2Dfrom keras.models import Modelfrom keras.optimizers import RMSprop, SGDfrom keras.callbacks import ModelCheckpointfrom keras.preprocessing.image import ImageDataGeneratorlearning_rate = 0.0001img_width = 299img_height = 299nbr_train_samples = 3019nbr_validation_samples = 758nbr_epochs = 25batch_size = 1nb_classes= 100train_data_dir = './train'val_data_dir = './val'DogNames = []for i in range(0,100):    DogNames.append(str(i))print('Loading InceptionV3 Weights ...')InceptionV3_notop = InceptionV3(include_top=False, weights='imagenet',                    input_tensor=None, input_shape=(299, 299, 3))print('Adding Average Pooling Layer and Softmax Output Layer ...')output = InceptionV3_notop.get_layer(index = -1).output  # Shape: (8, 8, 2048)output = AveragePooling2D((8, 8), strides=(8, 8), name='avg_pool')(output)output = Flatten(name='flatten')(output)output = Dense(nb_classes, activation='softmax', name='predictions')(output)InceptionV3_model = Model(InceptionV3_notop.input, output)optimizer = SGD(lr = learning_rate, momentum = 0.9, decay = 0.0, nesterov = True)InceptionV3_model.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])best_model_file = "./weights.h5"best_model = ModelCheckpoint(best_model_file, monitor='val_acc', verbose = 1, save_best_only = True)# 数据扩增train_datagen = ImageDataGenerator(        rescale=1./255,        shear_range=0.1,        zoom_range=0.1,        rotation_range=10.,        width_shift_range=0.1,        height_shift_range=0.1,        horizontal_flip=True)# this is the augmentation configuration we will use for validation:# only rescalingval_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(        train_data_dir,        target_size = (img_width, img_height),        batch_size = batch_size,        shuffle = True,        classes = DogNames,        class_mode = 'categorical'     )print train_generator.class_indicesvalidation_generator = val_datagen.flow_from_directory(        val_data_dir,        target_size=(img_width, img_height),        batch_size=batch_size,        shuffle = True,        classes = DogNames,        class_mode = 'categorical'        )InceptionV3_model.fit_generator(        train_generator,        samples_per_epoch = nbr_train_samples,        nb_epoch = nbr_epochs,        validation_data = validation_generator,        nb_val_samples = nbr_validation_samples,        callbacks = [best_model])

完成模型训练之后，直接测试错误率在0.21，此时对测试数据通过预处理生成多个图片进行测试，提升到0.20。

# test data generator for predictiontest_datagen = ImageDataGenerator(        rescale=1./255,        shear_range=0.1,        zoom_range=0.1,        width_shift_range=0.1,        height_shift_range=0.1,        horizontal_flip=True)print('Loading model and weights from training process ...')InceptionV3_model = load_model(weights_path)for idx in range(nbr_augmentation):    print('{}th augmentation for testing ...'.format(idx))    random_seed = np.random.random_integers(0, 100000)    test_generator = test_datagen.flow_from_directory(            test_data_dir,            target_size=(img_width, img_height),            batch_size=batch_size,            shuffle = False, # Important !!!            seed = random_seed,            classes = None,            class_mode = None)    test_image_list = test_generator.filenames    #print('image_list: {}'.format(test_image_list[:10]))    print('Begin to predict for testing data ...')    if idx == 0:        predictions = InceptionV3_model.predict_generator(test_generator, nbr_test_samples)    else:        predictions += InceptionV3_model.predict_generator(test_generator, nbr_test_samples)

进一步

1. 进行度量学习，减少类内距，增大类间距，如centerloss, tripletloss。由于centerloss每次需要得到同类的特征中心，同类的狗由于不同的位姿，会导致特征中心不稳定，效果不好
2. 将数据划分位10分，随机选取9份进行训练，一份测试，训练多个模型进行测试。
3. 将不同分类器得到的特征进行融合，拼接在一起训练。