utf8''Traffic-sign-recognition

所属分类人工智能/神经网络/深度学习
开发工具:Python
文件大小:420KB
下载次数:27
上传日期:2018-05-13 14:52:11
上 传 者lionkiss
说明:  项目基于Tensorflow进行实现。 #### 文件说明: --- * 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: --- * 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: --- * 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 Description: --- * 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 Description: --- * Here is a list of texts using the Belgian traffic sign data set, which can be found online, with 62 categories. #### Network Description: --- * 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)

文件列表:[举报垃圾]
Traffic-sign-recognition, 0 , 2018-04-17
Traffic-sign-recognition\README.md, 973 , 2018-04-17
Traffic-sign-recognition\img_result, 0 , 2018-04-17
Traffic-sign-recognition\img_result\testDemo.png, 157367 , 2018-04-17
Traffic-sign-recognition\img_result\train_result.png, 323552 , 2018-04-17
Traffic-sign-recognition\input_data.py, 2929 , 2018-04-17
Traffic-sign-recognition\testDemo.py, 4358 , 2018-04-17
Traffic-sign-recognition\traffic_sign_cnn.py, 4743 , 2018-04-17

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