The German Traffic Sign Detection Benchmark


The German Traffic Sign Detection Benchmark is a single-image detection assessment for researchers with interest in the field of computer vision, pattern recognition and image-based driver assistance. It is introduced on the IEEE International Joint Conference on Neural Networks 2013. It features ...



Competion Test Dataset

If you want to participate in our competition, please download the test dataset (530 MB) and process the images by your traffic sign detector. For details on the submission procedure, please refer to section "Submission format and regulations".


Feel free to download the full data set (1.6 GB) and use it for your purposes. The download package comprises

You can also still download the training and test datasets that were used for the competition ( training data set (1.1 GB), test data set (500 MB) ). Momentarily it is still possible to submit results on the test data set in order to compare your performance to that of other teams. For details on the submission procedure, please refer to section "Submission format and regulations".


Image format


Annotation format

Annotations are provided in CSV files. Fields are seperated by a semicolon (;). They contain the following information:

You can download source code that will help you to read and compare the annotation files with your detector's results. For an explanation of the class IDs we refer to the ReadMe.txt in the download package.


Submission format and regulations

Similar to the annotation format, the result files that can be submitted during the competition phase (schedule) are supposed to be provided in a CSV file format, seperated by a semicolon (;). The fields are

Hence, the result file will contain one line per detection. You can download and use source code with functions to write those result files (C++, Matlab).

In the submission section you will be asked to choose a category (prohibitive, danger, mandatory) for your detection results. Please note that any detection of a sign that is not in that category will be counted as a false detection. For submitting the results of your detector you will have to upload a zip file. The zip file should contain the result text files of several runs with different parametrizations. The results are then evaluated and a precision-recall plot is created thereof. The precision-recall plot is always computed with all files you submitted so far.

Code snippets




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