Advanced Artificial IntelligenceProject Practice:Advertising Signboard Classification
Advanced Artificial Intelligence Project Practice: Advertising Signboard Classification
IntroductionImagerecognition is oneof themosttypical applications fordeeplearning.Thereareallkindsof signs inreal lifeFor this experiment, select 1o common signage information in the real world, such as KFC, McDonald's, Nike etc.Eachtypeof signboard selects 10 to30 images as training data and 5-10images astest data.You needtobuild analgorithm model based on the training set and submit the experiment report and the final network model file肯德基R宝岛眼镜中中国电信SΛMSUNG三服电子务中心
Image recognition is one of the most typical applications for deep learning. There are all kinds of signs in real life. For this experiment, select 100 common signage information in the real world, such as KFC, McDonald's, Nike etc. Each type of signboard selects 10 to 30 images as training data and 5-10 images as test data. You need to build an algorithm model based on the training set and submit the experiment report and the final network model file. • Introduction
Datasets.Providetrainingdata sets,100 commonbillboards,and about10-30training imagespercategory.Provideimagefiles andtxtfiles.SELECTEDOb7b02087bf40067b020876400b7b0208766400676020876640C67b02087b6400b7b02087bf40.You can divide the verification set yourself.adid955addb5ad1aa2ba0685ad1aa83a32a5cad1sedbbe5esad1d1e594395cad1168877285cd2c11dfaBecce2c11dfaBecce9d2e11dfatecceZc1dfaBecceed2e11dfagecceZe11dfaGecteaf6a054513Experimentaldatasource0b7b02087bf400b7b02067bf40Gb7b02087bf40067b020876640Cb7b02087b640Gb46f21fbe096ad1f164755d5dad17e7e0f235cad1361a34515dad16566e0b75cd152591b725c663696586e802c11dfaBeccea2c11dfaBecceSc2c11dfa9ecce72c11dfaBecceeZc11dfaBecee6338744eafBac74%.Baidu-West Jiaotong University-BigDataSAECompetition2018-ClassificationandOb46121fbe0960b46/21fbe0960b46i21fbe0960b55b319ebc40b55b319ebc4Ob55b319ebc4b63fb44450907b6366dec9c307b639664b0710b745a0048d47cb745a147924ccb7452bf80931c338744ebfBaco7338744ebl8ac4fcte178b8215Sic1e178a8215338744eafBac54fc1e178b8215DetectionofMerchantSignboardsd9ca96ofMINIMINSO:SOU0b556319ebc40b556319ebc40b55b319ebc40655b319ebc40bd162d9f2d350bd162d9f2d35b7458cfaala3cb74512a1233acb7455ff5570fc3b745816ea707c72c0d7ea9478172c6f1e367d80fcle178a821544fc1e178a82153fc1e178a82154fcte178a821513632763d0c3813632763d0c391c424
• Provide training data sets, 100 common billboards, and about 10-30 training images per category. • Provide image files and txt files. • You can divide the verification set yourself. • Datasets • Baidu-West Jiaotong University·Big Data Compet it ion 2018——Classification and Detection of Merchant Signboards Experimental data source
·Experimentalenvironment.Keras+TensorFlow.YoucanchooseJupyternotebookorPycharmasIDE
• Keras+TensorFlow • You can choose Jupyter notebook or Pycharm as IDE. • Experimental environment
submit:Trainednetworkmodelfiles,includingmodels (jsonformat),modelweights (h5format)model_architecture_studentld.jsonVmodel.weights_studentld.h50.8m.ProjectVExperimentReportIncluding:The experimental result graph contains the curves of accuracy and loss,example:thgaVNetworkmodel structure,example:VModel identification accuracyIf the verification set is divided, the accuracy of the training set and the accuracy of the verification set need to be submitted.VIf the verification set is not divided, only the accuracy rate on the training set needs to be submitted
• Trained network model files, including models (json format), model weights (h5 format). ✓ model_architecture_studentId.json ✓ model_weights_studentId.h5 • Project • Experiment Report Including： ✓ The experimental result graph contains the curves of accuracy and loss. example: ✓ Network model structure ，example： ✓ Model identification accuracy ✓ If the verification set is divided, the accuracy of the training set and the accuracy of the verification set need to be submitted. ✓ If the verification set is not divided, only the accuracy rate on the training set needs to be submitted. • submit