[转]使用Keras进行图像分类
转自https://blog.csdn.net/u010632850/article/details/77102821
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 out def 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列的列表,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 cv2 import numpy as np from keras.utils import np_utils, conv_utils from keras.models import Sequential from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dropout, Dense, Activation from keras.optimizers import Adam import os import cPickle def 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 out def 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 1/10 6353/6353 [==============================] - 8s - loss: 4.2459 - acc: 0.1152 Epoch 2/10 6353/6353 [==============================] - 8s - loss: 3.8954 - acc: 0.1942 Epoch 3/10 6353/6353 [==============================] - 8s - loss: 3.6121 - acc: 0.2500 Epoch 4/10 6353/6353 [==============================] - 8s - loss: 3.3974 - acc: 0.2811 Epoch 5/10 6353/6353 [==============================] - 8s - loss: 3.2033 - acc: 0.3101 Epoch 6/10 6353/6353 [==============================] - 9s - loss: 3.0413 - acc: 0.3343 Epoch 7/10 6353/6353 [==============================] - 9s - loss: 2.9090 - acc: 0.3559 Epoch 8/10 6353/6353 [==============================] - 9s - loss: 2.7931 - acc: 0.3760 Epoch 9/10 6353/6353 [==============================] - 9s - loss: 2.7039 - acc: 0.3897 Epoch 10/10 6353/6353 [==============================] - 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')