项目基于Tensorflow进行实现。

#### 文件说明：
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*　　input_data.py:　图片的输入
*　　traffic_sign_cnn.py:　用cnn进行训练分类
*　　testDemo.py:　用于测试已经训练出来的模型，输入单个图片输出结果，并分类到文件夹


#### 数据集说明：
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*　　这里是列表文本使用的是比利时的交通标志数据集，可以网上自己找，里面有62个分类。

#### 网络说明：
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*　　这里是列表文本这里是列表文本CNN网络包含两个卷积层，两个全连接层。识别率大概在95% 左右，可以自己根据需要自己修改参数提高识别率




另外，训练开始前需要先在项目目录下新建文件夹./log/train/,用来保存模型参数,数据集的目录结构大概是./data/train/00001（标签）/图片(The project is based on Tensorflow.

#### File Descr iption:
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* input_data.py: input of the picture
* traffic_sign_cnn.py: Training classification with cnn
* testDemo.py: Used to test the trained model, enter the result of a single image output, and categorize it into a folder


#### Data Set Descr iption:
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* Here is a list of texts using the Belgian traffic sign data set, which can be found online, with 62 categories.

#### Network Descr iption:
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* Here is the list text Here is the list text The CNN network consists of two convolutional layers, two fully connected layers. The recognition rate is about 95%, you can modify the parameters by yourself to improve the recognition rate




In addition, before the start of training, you need to create a new folder in the project directory. / Log / train /, used to save the model parameters, the directory structure of the data set is probably ./data/train/00001 (tag) / picture)