Advanced Artificial Intelligence Project Practice Advertising Signboard Classification

Advanced Artificial Intelligence Project Practice: Advertising Signboard Classification

• Introduction Image recognition is one of the most typical applications for deep learning. There all kinds of signs in real life. For this experiment, select 100 common signage information in the real world, such as KFC, Mc. Donald'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.

• Datasets • 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. Experimental data source • Baidu-West Jiaotong University·Big Data Competition 2018——Classification Detection of Merchant Signboards and

• Experimental environment • Keras+Tensor. Flow • You can choose Jupyter notebook or Pycharm as IDE.

• submit • Trained network model files, including models (json format), model weights (h 5 format). ü model_architecture_student. Id. json ü model_weights_student. Id. h 5 • Project • Experiment Report Including： ü The experimental result graph contains the curves of accuracy and loss. example: ü Network model structure ，example： ü Model identification accuracy ü If the verification set is divided, the accuracy of the training set and the accuracy of the verification set need to be submitted. ü If the verification set is not divided, only the accuracy rate on the training set needs to be submitted.

• Reference # Network model display model. summary() plot_model(model, to_file=‘your/project/path/model. png')

• Reference # Save the model structure as a json file json_string = model. to_json() open(' your/project/path/model_architecture. json', 'w'). write(json_string) # Save model parameters to h 5 file model. save_weights(' your/project/path/model_weights. h 5')

• Note ü This experiment is based on the recognition accuracy of the model on the test set and the experimental report. ü No plagiarism. ü I hope that you will try to design a new network model to solve the image recognition task and learn the way of tuning and some suppression methods of overfitting. ü If the data processing method, the principle of model design, and the method of adjusting the parameters can be reflected in the experimental report, extra points will be considered.

• data Link：https: //pan. baidu. com/s/1 YKGOL 5 r. Le. IGR-5 Fvpji 7 y. Q Password： 4 v 5 s