kaggle比赛--Dog Breed Identification狗狗品种识别大赛

对截止至2020.8.1的top5方案进行总结归纳
比赛链接：dog-breed-identification
1、数据形式
2、top1总结
3、top2总结
4、top3总结
……

1、数据形式

120个类别，训练集共10222张图片，测试集10357张

具体：train和test根目录下为图片，labels为每个图片对应label，sample_submission为测试集和最终给出的csv格式范例。具体如下

import pandas as pd
labelfile = os.path.join(dirname,'labels.csv')
dataf = pd.read_csv(labelfile)
print('csv文件长度：', len(dataf))
dataf

# 载入测试集
df2 = pd.read_csv(dirname + 'sample_submission.csv')
df2.head()

2、top1方案总结

top1传送门
方案：
1、使用4个提取特征器提取特征（fc层之前的特征）
2、将特征融合
3、使用简单的cnn网络，包括dense层和dropout层进行训练
没有使用数据增强
结果：验证集最高为0.9413，loss最低为0.1982。

# 提取特征网络
def get_features(model_name, data_preprocessor, input_size, data):'''1- Create a feature extractor to extract features from the data.2- Returns the extracted features and the feature extractor.'''#Prepare pipeline.input_layer = Input(input_size)preprocessor = Lambda(data_preprocessor)(input_layer)base_model = model_name(weights='imagenet', include_top=False,input_shape=input_size)(preprocessor)avg = GlobalAveragePooling2D()(base_model)feature_extractor = Model(inputs = input_layer, outputs = avg)#Extract feature.feature_maps = feature_extractor.predict(data, batch_size=64, verbose=1)print('Feature maps shape: ', feature_maps.shape)return feature_maps# 使用# Extract features using InceptionV3 as extractor.
from keras.applications.inception_v3 import InceptionV3, preprocess_input
inception_preprocessor = preprocess_input
inception_features = get_features(InceptionV3,inception_preprocessor,img_size, X)
...
# 依次提取了InceptionV3、xception、NASNetLarge 、InceptionResNetV2 四个网络特征

# 将特征concate在一起，大小为(10222、9664)
final_features = np.concatenate([inception_features,xception_features,nasnet_features,inc_resnet_features,], axis=-1)
print('Final feature maps shape', final_features.shape)

#Prepare DNN model
dnn = keras.models.Sequential([InputLayer(final_features.shape[1:]),Dropout(0.7),Dense(n_classes, activation='softmax')
])dnn.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])

注意：测试时也同样需要提取并concate特征后再predict

3、top2方案总结

top2传送门
方案：和上面一样，只不过换了提取特征网络为InceptionV3、xception、NASNetLarge、InceptionResNetV2

4、top3方案总结

top3传送门
受到欺骗，方案和上面一样

5、top4方案总结

top4传送门
1、数据预处理及数据增强
2、构建模型：在InceptionResNetV2基础上，去除原fc层，pool后添加三层fc层
结果：train_acc=75.623%, train_loss=0.87319，val_acc=89.232%, val_loss=0.09762

norm_factor = 1 / 255#
# Augmentation Ranges
transform_params = {'featurewise_center': False,'featurewise_std_normalization': False,'samplewise_center': False,'samplewise_std_normalization': False,'rotation_range': 30, 'width_shift_range': 0.15,'height_shift_range': 0.15,'horizontal_flip': True,'rescale': norm_factor
}# the generator used for training - gives augmented images
img_gen = ImageDataGenerator(**transform_params)

值得借鉴的代码：将数据增强后的数据plot出来，及写一个plotter的class，将每个epoch的loss、acc等描绘出来

# a Fully connected layer with activation, batchnorm and dropout
def dense_block(x, neurons, layer_no):x = Dense(neurons, kernel_initializer=he_normal(layer_no), name=f'topDense{layer_no}')(x)x = Activation('relu', name=f'Relu{layer_no}')(x)x = BatchNormalization(name=f'BatchNorm{layer_no}')(x)x = Dropout(0.5, name=f'Dropout{layer_no}')(x)return xdef create_model(shape):input_layer = Input(shape, name='input_layer')  # input layer with given shape# load InceptionResNetV2 with initialized weights and remove final dense layers - frozen layersincep_res = InceptionResNetV2(include_top=False, weights='imagenet', input_tensor=input_layer)for layer in incep_res.layers:layer.trainable = False# pooling to reduce dimensionality of each feature mappool = MaxPooling2D(pool_size=[3, 3], strides=[3, 3], padding='same')(incep_res.output)flat1 = Flatten(name='Flatten1')(pool)flat1_bn = BatchNormalization(name='BatchNormFlat')(flat1)# dense layers after the InceptionResNetV2 initialized layersdens1 = dense_block(flat1_bn, neurons=512, layer_no=1)dens2 = dense_block(dens1, neurons=512, layer_no=2)dens3 = dense_block(dens2, neurons=1024, layer_no=3)dens_final = Dense(classes_num, name='Dense4')(dens3)output_layer = Activation('softmax', name='Softmax')(dens_final)model = Model(inputs=[input_layer], outputs=[output_layer])return model

6、top5方案总结

top5传送门
使用fastai，resnet101网络及预训练
1、resnet101 baseline，acc为0.869
2、precompute+data augmentation, acc为0.889
3、扩大输入尺寸，acc为0.891