使用Keras进行图像分类

Keras深度学习框架可以用来了解深度学习可以用来做什么，下面介绍一些使用Keras来做基础的图像分类的内容，欢迎各位交流。

参考资料：https://morvanzhou.github.io/tutorials/machine-learning/keras/2-3-CNN/

我使用的版本：Python2.7，numpy1.13.1，Theano0.9.0，Keras2.0.6，h5py2.5.0，opencv2.4.13，WIN7系统。

 

要做图像分类，首先需要有数据集，需要将下载到的图像数据集转化为Keras可以识别的numpy矩阵。需要得到X_train，X_test，y_train，y_test，其中X_train和X_test分别是一个4维矩阵，第一维代表有几幅图像，后三维代表图像数据，y_train和y_test是一维列表，表示对应的图像属于哪一类。

 

可以下载到的图像数据集一般分为两种，一种是由若干文件夹组成，每个文件夹的名字是该类别的名字，每个文件夹中包含若干图像，这种数据集需要自己划分训练集和测试集；另一种由train文件夹和test文件夹组成，每个文件夹中有一些文件夹，其名字是类别的名字，其中有很多的图像，这种则固定了训练集和测试集。有时候数据集中会有文件来说明图像的名字和对应的标注，但是对于图像分类来说，不需要这些标注也可以提取出需要的numpy矩阵。

 

这里使用简单的网络对Caltech101数据集进行分类，这里暂时不考虑去除背景类，经过简单的改动后也可对cifar10数据集进行分类。如果需要更高的准确率，需要修改所用的网络。

提取的方法如下：（get_data和get_2data函数分别对应上面说的两种数据集。）

def eachFile(filepath):                 #将目录内的文件名放入列表中    pathDir =  os.listdir(filepath)    out = []    for allDir in pathDir:        child = allDir.decode('gbk')    # .decode('gbk')是解决中文显示乱码问题        out.append(child)    return outdef get_data(data_name,train_percentage=0.7,resize=True,data_format=None):   #从文件夹中获取图像数据    file_name = os.path.join(pic_dir_out,data_name+str(Width)+"X"+str(Height)+".pkl")       if os.path.exists(file_name):           #判断之前是否有存到文件中        (X_train, y_train), (X_test, y_test) = cPickle.load(open(file_name,"rb"))        return (X_train, y_train), (X_test, y_test)      data_format = conv_utils.normalize_data_format(data_format)    pic_dir_set = eachFile(pic_dir_data)      X_train = []    y_train = []    X_test = []    y_test = []    label = 0    for pic_dir in pic_dir_set:        print pic_dir_data+pic_dir        if not os.path.isdir(os.path.join(pic_dir_data,pic_dir)):            continue            pic_set = eachFile(os.path.join(pic_dir_data,pic_dir))        pic_index = 0        train_count = int(len(pic_set)*train_percentage)        for pic_name in pic_set:            if not os.path.isfile(os.path.join(pic_dir_data,pic_dir,pic_name)):                continue            img = cv2.imread(os.path.join(pic_dir_data,pic_dir,pic_name))            if img is None:                continue            img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)             if (resize):                img = cv2.resize(img,(Width,Height))            if (data_format == 'channels_last'):                img = img.reshape(-1,Width,Height,1)            elif (data_format == 'channels_first'):                img = img.reshape(-1,1,Width,Height)            if (pic_index < train_count):                X_train.append(img)                y_train.append(label)                      else:                X_test.append(img)                y_test.append(label)            pic_index += 1        if len(pic_set) <> 0:                    label += 1    X_train = np.concatenate(X_train,axis=0)            X_test = np.concatenate(X_test,axis=0)        y_train = np.array(y_train)    y_test = np.array(y_test)    cPickle.dump([(X_train, y_train), (X_test, y_test)],open(file_name,"wb"))     return (X_train, y_train), (X_test, y_test)   def get_2data(data_name,resize=True,data_format=None):   #当数据被分为train和test两个部分时使用    file_name = os.path.join(pic_dir_out,data_name+str(Width)+"X"+str(Height)+".pkl")       if os.path.exists(file_name):           #判断之前是否有存到文件中        (X_train, y_train), (X_test, y_test) = cPickle.load(open(file_name,"rb"))        return (X_train, y_train), (X_test, y_test)       data_format = conv_utils.normalize_data_format(data_format)    all_dir_set = eachFile(pic_dir_data)    X_train = []    y_train = []    X_test = []    y_test = []    for all_dir in all_dir_set:        if not os.path.isdir(os.path.join(pic_dir_data,all_dir)):            continue        label = 0        pic_dir_set = eachFile(os.path.join(pic_dir_data,all_dir))        for pic_dir in pic_dir_set:            print pic_dir_data+pic_dir            if not os.path.isdir(os.path.join(pic_dir_data,all_dir,pic_dir)):                continue                pic_set = eachFile(os.path.join(pic_dir_data,all_dir,pic_dir))            for pic_name in pic_set:                if not os.path.isfile(os.path.join(pic_dir_data,all_dir,pic_dir,pic_name)):                    continue                img = cv2.imread(os.path.join(pic_dir_data,all_dir,pic_dir,pic_name))                if img is None:                    continue                img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)                 if resize:                    img = cv2.resize(img,(Width,Height))                if (data_format == 'channels_last'):                    img = img.reshape(-1,Width,Height,1)                elif (data_format == 'channels_first'):                    img = img.reshape(-1,1,Width,Height)                if ('train' in all_dir):                    X_train.append(img)                    y_train.append(label)                          elif ('test' in all_dir):                    X_test.append(img)                    y_test.append(label)            if len(pic_set) <> 0:                        label += 1    X_train = np.concatenate(X_train,axis=0)            X_test = np.concatenate(X_test,axis=0)        y_train = np.array(y_train)    y_test = np.array(y_test)    cPickle.dump([(X_train, y_train), (X_test, y_test)],open(file_name,"wb"))     return (X_train, y_train), (X_test, y_test)   

其中的一些参数值为

    Width = 32    Height = 32    num_classes = 102            pic_dir_out = 'E:/pic_cnn/pic_out/'      pic_dir_data = 'E:/pic_cnn/pic_dataset/Caltech101/'  

如果每次都要遍历这些文件夹，获得numpy矩阵，还是比较慢的，通过文件存取的方式，可以将提取到的矩阵存成文件，之后运行的时候就可以较快的运行。

接下来需要对数据做预处理，先将图像数值转换到0到1之间，如果不这样做准确率会下降。np_utils.to_categorical的用途是，假设图像分为10类，得到的y_train和y_test就是0到9的数字组成的列表，需要将它做一个变换，例如其中的数字5，表示第6类，变化之后为[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]，第6位取值为1。原因是之后得到的对每幅图像的预测结果，也是一个10维的列表，例如[0, 0, 0, 0.1, 0, 0.8, 0, 0.1, 0, 0]，其中的最大值如果和实际值是在同一位，说明预测准确。

    X_train = X_train/255.              #数据预处理    X_test = X_test/255.    print X_train.shape    print X_test.shape    y_train = np_utils.to_categorical(y_train, num_classes)    y_test = np_utils.to_categorical(y_test, num_classes)

之后就可以使用Keras构建一些简单的CNN结构。

所设计的CNN结构代码如下：

    model = Sequential()                #CNN构建    model.add(Convolution2D(        input_shape=(Width, Height, 1),        #input_shape=(1, Width, Height),        filters=8,        kernel_size=3,        strides=1,        padding='same',             data_format='channels_last',    ))    model.add(Activation('relu'))    model.add(MaxPooling2D(        pool_size=2,        strides=2,        data_format='channels_last',    ))    model.add(Convolution2D(16, 3, strides=1, padding='same', data_format='channels_last'))    model.add(Activation('relu'))    model.add(MaxPooling2D(2, 2, data_format='channels_last'))    model.add(Dropout(0.5))    model.add(Flatten())    model.add(Dense(256, activation='relu'))    model.add(Dropout(0.5))        model.add(Dense(num_classes, activation='softmax'))        model.compile(optimizer=Adam(),                  loss='categorical_crossentropy',                  metrics=['accuracy'])

Convolution2D层（卷积层）相当于用卷积核去扫描原图像，得到一些新的图像。其中的参数fliters表示卷积核的数量，也就是得到的新图像的数量，kernel_size是卷积核的大小，strides是每次扫描移动几个像素，padding表示是否通过在图像周围加一圈0，使得生成的卷积图像大小与原图像相同，’same’表示加一圈0，默认’valid’表示不加，data_format表示图像的通道位于3维中的前面还是后面。另外，第一个层需要注明input_shape，表示输入图像的大小。

Activation层（激励函数）是一个函数，将前面传递过来的值做一个变换，这个函数需要有导数，常用的有relu，softmax。relu是当x小于0时，y等于0，当x大于0时，y等于x。

MaxPooling2D层（池化层）将前面得到的卷积图像，用一个小方格来扫描，每个方格中只记录它的最大值，扫描结束之后会产生新的小一些的图像。pool_size表示方格的大小，strides表示每次移动的长度，如果都为2则会使图像的长和宽都除以2。

Flatten层是将图像展平成一维的列表。

Dense层（全连接层）可以使参数的数量发生变化，参数units表示该层有多少个神经元，可以改变输出结果的维度。

Dropout层表示对其相邻的两层训练参数时，会随机的丢弃一定百分比的神经元的连接，减少过拟合的现象。

softmax激励函数可以将输出的结果转化为0到1之间的浮点数，同一个列表中所有数值的和为1，可以当作是分为该类的概率。

 

结构设计好之后，需要通过compile函数定义一些优化参数的方式。

optimizer表示梯度下降是选用哪种优化器来优化参数，loss表示损失值的计算使用哪种方式，metrics表示对测试数据evaluate时，性能评估的方法。

 

然后就可以使用训练数据进行训练了。训练过程如下：

    print('\nTraining ------------')    #从文件中提取参数，训练后存在新的文件中    cm = 0    cm_str = '' if cm==0 else str(cm)    cm2_str = '' if (cm+1)==0 else str(cm+1)      if cm >= 1:        model.load_weights(os.path.join(pic_dir_out,'cnn_model_Caltech101_'+cm_str+'.h5'))        #model.load_weights(os.path.join(pic_dir_out,'cnn_model_Cifar10_'+cm_str+'.h5'))        model.fit(X_train, y_train, epochs=10, batch_size=128,)   #正式训练数据    model.save_weights(os.path.join(pic_dir_out,'cnn_model_Caltech101_'+cm2_str+'.h5'))

epochs参数表示总共进行多少轮训练，batch_size表示每次梯度更新会用到多少组数据。这里增加了一些小的操作的用途是，每次训练完网络的参数后保存成文件，递增修改cm的值后再运行可以先读取上次训练的参数，然后再接着训练。我在运行代码的过程中发现，该程序消耗的内存会不断增加，使得epochs的值不能取一个非常大的值，所以只能多次运行才能得到收敛的结果。暂时不清楚有没有办法减小内存的消耗。

 

最后是对测试数据进行预测，并评估结果。这里得到模型最终的损失值和准确率，以及top-N的准确率，和每个类别的准确率。

    print('\nTesting ------------')     #对测试集进行评估，额外获得metrics中的信息    loss, accuracy = model.evaluate(X_test, y_test)    print('\n')    print('test loss: ', loss)    print('test accuracy: ', accuracy)        class_name_list = get_name_list(pic_dir_data)   #获取每一类的名字列表     pred = model.predict(X_test, batch_size=128)    #获取top-N的每类的准确率    N = 5    pred_list = []    for row in pred:        pred_list.append(row.argsort()[-N:][::-1])  #获取最大的N个值的下标    pred_array = np.array(pred_list)    test_arg = np.argmax(y_test,axis=1)    class_count = [0 for _ in xrange(num_classes)]    class_acc = [0 for _ in xrange(num_classes)]    for i in xrange(len(test_arg)):        class_count[test_arg[i]] += 1        if test_arg[i] in pred_array[i]:            class_acc[test_arg[i]] += 1    print('top-'+str(N)+' all acc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))    for i in xrange(num_classes):        print (i, class_name_list[i], 'acc: '+str(class_acc[i])+'/'+str(class_count[i]))

完整代码如下：

import cv2import numpy as npfrom keras.utils import np_utils, conv_utilsfrom keras.models import Sequentialfrom keras.layers import Convolution2D, MaxPooling2D, Flatten, Dropout, Dense, Activationfrom keras.optimizers import Adamimport osimport cPickledef get_name_list(filepath):                #获取各个类别的名字    pathDir =  os.listdir(filepath)    out = []    for allDir in pathDir:        if os.path.isdir(os.path.join(filepath,allDir)):            child = allDir.decode('gbk')    # .decode('gbk')是解决中文显示乱码问题            out.append(child)    return out    def eachFile(filepath):                 #将目录内的文件名放入列表中    pathDir =  os.listdir(filepath)    out = []    for allDir in pathDir:        child = allDir.decode('gbk')    # .decode('gbk')是解决中文显示乱码问题        out.append(child)    return outdef get_data(data_name,train_percentage=0.7,resize=True,data_format=None):   #从文件夹中获取图像数据    file_name = os.path.join(pic_dir_out,data_name+str(Width)+"X"+str(Height)+".pkl")       if os.path.exists(file_name):           #判断之前是否有存到文件中        (X_train, y_train), (X_test, y_test) = cPickle.load(open(file_name,"rb"))        return (X_train, y_train), (X_test, y_test)      data_format = conv_utils.normalize_data_format(data_format)    pic_dir_set = eachFile(pic_dir_data)      X_train = []    y_train = []    X_test = []    y_test = []    label = 0    for pic_dir in pic_dir_set:        print pic_dir_data+pic_dir        if not os.path.isdir(os.path.join(pic_dir_data,pic_dir)):            continue            pic_set = eachFile(os.path.join(pic_dir_data,pic_dir))        pic_index = 0        train_count = int(len(pic_set)*train_percentage)        for pic_name in pic_set:            if not os.path.isfile(os.path.join(pic_dir_data,pic_dir,pic_name)):                continue            img = cv2.imread(os.path.join(pic_dir_data,pic_dir,pic_name))            if img is None:                continue            img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)             if (resize):                img = cv2.resize(img,(Width,Height))            if (data_format == 'channels_last'):                img = img.reshape(-1,Width,Height,1)            elif (data_format == 'channels_first'):                img = img.reshape(-1,1,Width,Height)            if (pic_index < train_count):                X_train.append(img)                y_train.append(label)                      else:                X_test.append(img)                y_test.append(label)            pic_index += 1        if len(pic_set) <> 0:                    label += 1    X_train = np.concatenate(X_train,axis=0)            X_test = np.concatenate(X_test,axis=0)        y_train = np.array(y_train)    y_test = np.array(y_test)    cPickle.dump([(X_train, y_train), (X_test, y_test)],open(file_name,"wb"))     return (X_train, y_train), (X_test, y_test)   def get_2data(data_name,resize=True,data_format=None):   #当train和test数据被分为两个部分时使用    file_name = os.path.join(pic_dir_out,data_name+str(Width)+"X"+str(Height)+".pkl")       if os.path.exists(file_name):           #判断之前是否有存到文件中        (X_train, y_train), (X_test, y_test) = cPickle.load(open(file_name,"rb"))        return (X_train, y_train), (X_test, y_test)       data_format = conv_utils.normalize_data_format(data_format)    all_dir_set = eachFile(pic_dir_data)    X_train = []    y_train = []    X_test = []    y_test = []    for all_dir in all_dir_set:        if not os.path.isdir(os.path.join(pic_dir_data,all_dir)):            continue        label = 0        pic_dir_set = eachFile(os.path.join(pic_dir_data,all_dir))        for pic_dir in pic_dir_set:            print pic_dir_data+pic_dir            if not os.path.isdir(os.path.join(pic_dir_data,all_dir,pic_dir)):                continue                pic_set = eachFile(os.path.join(pic_dir_data,all_dir,pic_dir))            for pic_name in pic_set:                if not os.path.isfile(os.path.join(pic_dir_data,all_dir,pic_dir,pic_name)):                    continue                img = cv2.imread(os.path.join(pic_dir_data,all_dir,pic_dir,pic_name))                if img is None:                    continue                img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)                 if resize:                    img = cv2.resize(img,(Width,Height))                if (data_format == 'channels_last'):                    img = img.reshape(-1,Width,Height,1)                elif (data_format == 'channels_first'):                    img = img.reshape(-1,1,Width,Height)                if ('train' in all_dir):                    X_train.append(img)                    y_train.append(label)                          elif ('test' in all_dir):                    X_test.append(img)                    y_test.append(label)            if len(pic_set) <> 0:                        label += 1    X_train = np.concatenate(X_train,axis=0)            X_test = np.concatenate(X_test,axis=0)        y_train = np.array(y_train)    y_test = np.array(y_test)    cPickle.dump([(X_train, y_train), (X_test, y_test)],open(file_name,"wb"))     return (X_train, y_train), (X_test, y_test)   def main():    global Width, Height, pic_dir_out, pic_dir_data    Width = 32    Height = 32    num_classes = 102                   #Caltech101为102  cifar10为10    pic_dir_out = 'E:/pic_cnn/pic_out/'      pic_dir_data = 'E:/pic_cnn/pic_dataset/Caltech101/'      (X_train, y_train), (X_test, y_test) = get_data("Caltech101_gray_data_",0.7,data_format='channels_last')    #pic_dir_data = 'E:/pic_cnn/pic_dataset/cifar10/'    #(X_train, y_train), (X_test, y_test) = get_2data("Cifar10_gray_data_",resize=False,data_format='channels_last')        X_train = X_train/255.              #数据预处理    X_test = X_test/255.    print X_train.shape    print X_test.shape    y_train = np_utils.to_categorical(y_train, num_classes)    y_test = np_utils.to_categorical(y_test, num_classes)        model = Sequential()                #CNN构建    model.add(Convolution2D(        input_shape=(Width, Height, 1),        #input_shape=(1, Width, Height),        filters=8,        kernel_size=3,        strides=1,        padding='same',             data_format='channels_last',    ))    model.add(Activation('relu'))    model.add(MaxPooling2D(        pool_size=2,        strides=2,        data_format='channels_last',    ))    model.add(Convolution2D(16, 3, strides=1, padding='same', data_format='channels_last'))    model.add(Activation('relu'))    model.add(MaxPooling2D(2, 2, data_format='channels_last'))    model.add(Dropout(0.5))    model.add(Flatten())    model.add(Dense(256, activation='relu'))    model.add(Dropout(0.5))        model.add(Dense(num_classes, activation='softmax'))        model.compile(optimizer=Adam(),                  loss='categorical_crossentropy',                  metrics=['accuracy'])            print('\nTraining ------------')    #从文件中提取参数，训练后存在新的文件中    cm = 0                              #修改这个参数可以多次训练    cm_str = '' if cm==0 else str(cm)    cm2_str = '' if (cm+1)==0 else str(cm+1)      if cm >= 1:        model.load_weights(os.path.join(pic_dir_out,'cnn_model_Caltech101_'+cm_str+'.h5'))        #model.load_weights(os.path.join(pic_dir_out,'cnn_model_Cifar10_'+cm_str+'.h5'))        model.fit(X_train, y_train, epochs=10, batch_size=128,)   #正式训练数据    model.save_weights(os.path.join(pic_dir_out,'cnn_model_Caltech101_'+cm2_str+'.h5'))         print('\nTesting ------------')     #对测试集进行评估，额外获得metrics中的信息    loss, accuracy = model.evaluate(X_test, y_test)    print('\n')    print('test loss: ', loss)    print('test accuracy: ', accuracy)        class_name_list = get_name_list(pic_dir_data)    #获取top-N的每类的准确率    #class_name_list = get_name_list(os.path.join(pic_dir_data,'train'))    pred = model.predict(X_test, batch_size=128)    N = 5    pred_list = []    for row in pred:        pred_list.append(row.argsort()[-N:][::-1])  #获取最大的N个值的下标    pred_array = np.array(pred_list)    test_arg = np.argmax(y_test,axis=1)    class_count = [0 for _ in xrange(num_classes)]    class_acc = [0 for _ in xrange(num_classes)]    for i in xrange(len(test_arg)):        class_count[test_arg[i]] += 1        if test_arg[i] in pred_array[i]:            class_acc[test_arg[i]] += 1    print('top-'+str(N)+' all acc:',str(sum(class_acc))+'/'+str(len(test_arg)),sum(class_acc)/float(len(test_arg)))    for i in xrange(num_classes):        print (i, class_name_list[i], 'acc: '+str(class_acc[i])+'/'+str(class_count[i]))    if __name__ == '__main__':    main()

运行结果如下：

(6353, 32, 32, 1)(2792, 32, 32, 1)Training ------------Epoch 1/106353/6353 [==============================] - 8s - loss: 4.2459 - acc: 0.1152     Epoch 2/106353/6353 [==============================] - 8s - loss: 3.8954 - acc: 0.1942     Epoch 3/106353/6353 [==============================] - 8s - loss: 3.6121 - acc: 0.2500     Epoch 4/106353/6353 [==============================] - 8s - loss: 3.3974 - acc: 0.2811     Epoch 5/106353/6353 [==============================] - 8s - loss: 3.2033 - acc: 0.3101     Epoch 6/106353/6353 [==============================] - 9s - loss: 3.0413 - acc: 0.3343     Epoch 7/106353/6353 [==============================] - 9s - loss: 2.9090 - acc: 0.3559     Epoch 8/106353/6353 [==============================] - 9s - loss: 2.7931 - acc: 0.3760     Epoch 9/106353/6353 [==============================] - 9s - loss: 2.7039 - acc: 0.3897     Epoch 10/106353/6353 [==============================] - 9s - loss: 2.6152 - acc: 0.4003     Testing ------------2720/2792 [============================>.] - ETA: 0s('test loss: ', 2.5188725370177227)('test accuracy: ', 0.42836676217765041)('top-5 all acc:', '1754/2792', 0.6282234957020058)(0, u'0.accordion', 'acc: 15/17')(1, u'1.airplanes', 'acc: 238/240')(2, u'10.brain', 'acc: 7/30')(3, u'100.wrench', 'acc: 5/12')(4, u'101.yin_yang', 'acc: 15/18')(5, u'11.brontosaurus', 'acc: 7/13')(6, u'12.buddha', 'acc: 9/26')(7, u'13.butterfly', 'acc: 6/28')(8, u'14.camera', 'acc: 5/15')(9, u'15.cannon', 'acc: 0/13')(10, u'16.car_side', 'acc: 37/37')(11, u'17.ceiling_fan', 'acc: 1/15')(12, u'18.cellphone', 'acc: 16/18')(13, u'19.chair', 'acc: 4/19')(14, u'2.anchor', 'acc: 2/13')(15, u'20.chandelier', 'acc: 27/33')(16, u'21.cougar_body', 'acc: 0/15')(17, u'22.cougar_face', 'acc: 8/21')(18, u'23.crab', 'acc: 4/22')(19, u'24.crayfish', 'acc: 3/21')(20, u'25.crocodile', 'acc: 0/15')(21, u'26.crocodile_head', 'acc: 1/16')(22, u'27.cup', 'acc: 3/18')(23, u'28.dalmatian', 'acc: 14/21')(24, u'29.dollar_bill', 'acc: 14/16')(25, u'3.ant', 'acc: 0/13')(26, u'30.dolphin', 'acc: 5/20')(27, u'31.dragonfly', 'acc: 12/21')(28, u'32.electric_guitar', 'acc: 15/23')(29, u'33.elephant', 'acc: 14/20')(30, u'34.emu', 'acc: 0/16')(31, u'35.euphonium', 'acc: 8/20')(32, u'36.ewer', 'acc: 7/26')(33, u'37.Faces', 'acc: 127/131')(34, u'38.Faces_easy', 'acc: 127/131')(35, u'39.ferry', 'acc: 10/21')(36, u'4.BACKGROUND_Google', 'acc: 133/141')(37, u'40.flamingo', 'acc: 9/21')(38, u'41.flamingo_head', 'acc: 0/14')(39, u'42.garfield', 'acc: 6/11')(40, u'43.gerenuk', 'acc: 0/11')(41, u'44.gramophone', 'acc: 4/16')(42, u'45.grand_piano', 'acc: 24/30')(43, u'46.hawksbill', 'acc: 17/30')(44, u'47.headphone', 'acc: 3/13')(45, u'48.hedgehog', 'acc: 4/17')(46, u'49.helicopter', 'acc: 17/27')(47, u'5.barrel', 'acc: 4/15')(48, u'50.ibis', 'acc: 10/24')(49, u'51.inline_skate', 'acc: 5/10')(50, u'52.joshua_tree', 'acc: 11/20')(51, u'53.kangaroo', 'acc: 15/26')(52, u'54.ketch', 'acc: 26/35')(53, u'55.lamp', 'acc: 8/19')(54, u'56.laptop', 'acc: 12/25')(55, u'57.Leopards', 'acc: 58/60')(56, u'58.llama', 'acc: 9/24')(57, u'59.lobster', 'acc: 0/13')(58, u'6.bass', 'acc: 1/17')(59, u'60.lotus', 'acc: 12/20')(60, u'61.mandolin', 'acc: 2/13')(61, u'62.mayfly', 'acc: 1/12')(62, u'63.menorah', 'acc: 19/27')(63, u'64.metronome', 'acc: 6/10')(64, u'65.minaret', 'acc: 21/23')(65, u'66.Motorbikes', 'acc: 237/240')(66, u'67.nautilus', 'acc: 3/17')(67, u'68.octopus', 'acc: 0/11')(68, u'69.okapi', 'acc: 6/12')(69, u'7.beaver', 'acc: 3/14')(70, u'70.pagoda', 'acc: 15/15')(71, u'71.panda', 'acc: 2/12')(72, u'72.pigeon', 'acc: 4/14')(73, u'73.pizza', 'acc: 4/16')(74, u'74.platypus', 'acc: 1/11')(75, u'75.pyramid', 'acc: 8/18')(76, u'76.revolver', 'acc: 19/25')(77, u'77.rhino', 'acc: 3/18')(78, u'78.rooster', 'acc: 11/15')(79, u'79.saxophone', 'acc: 0/12')(80, u'8.binocular', 'acc: 6/10')(81, u'80.schooner', 'acc: 14/19')(82, u'81.scissors', 'acc: 4/12')(83, u'82.scorpion', 'acc: 2/26')(84, u'83.sea_horse', 'acc: 1/18')(85, u'84.snoopy', 'acc: 3/11')(86, u'85.soccer_ball', 'acc: 10/20')(87, u'86.stapler', 'acc: 6/14')(88, u'87.starfish', 'acc: 9/26')(89, u'88.stegosaurus', 'acc: 4/18')(90, u'89.stop_sign', 'acc: 9/20')(91, u'9.bonsai', 'acc: 26/39')(92, u'90.strawberry', 'acc: 3/11')(93, u'91.sunflower', 'acc: 8/26')(94, u'92.tick', 'acc: 9/15')(95, u'93.trilobite', 'acc: 26/26')(96, u'94.umbrella', 'acc: 13/23')(97, u'95.watch', 'acc: 62/72')(98, u'96.water_lilly', 'acc: 1/12')(99, u'97.wheelchair', 'acc: 11/18')(100, u'98.wild_cat', 'acc: 0/11')(101, u'99.windsor_chair', 'acc: 8/17')