广东工业智造大数据创新大赛

标签：oss   article   layer   rac   block   chm   regular   png   sdn   

competition questions and data

guangdong_defect_instruction_20180916.xlsx
guangdong_round1_submit_sample_20180916.csv
guangdong_round1_test_a_20180916.zip
guangdong_round1_train1_20180903.zip

Solutions

Using Kaggle cat and dog classification code,
even using there depth deeping networks ResNet50,Inception V3,
Xception to extract image features,
and using neural networkf DNN classification,
verification set shows over-fitting.

Kaggle cat and dog classification

ResNet50

resnetv2-50

tensorflow.Keras use Resnet50 to realize CatDogDistinguish

比赛思路

Direct image classificaton,select a network to extract features,followed by a fully connection layer classification,plus regularization to reduce over-fitting.Then let go of all levels of training.The final accuracy is about 0.92,in fact,as long as the default parameters do not depart from the spectrum on the line,adjusting the parameters does not have much impact on the results.

select a network to extract features

competition solution 2：Standard DenseNet,softmax12 classification,
made data enhancement;
tried to tune learning_rate,
batch_size,num_layers

DenseNet

广东工业智造大数据创新大赛

标签：oss   article   layer   rac   block   chm   regular   png   sdn   

原文地址：https://www.cnblogs.com/hugeng007/p/9740702.html