GTSRB Dataset

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Visualization of the GTSRB dataset in the Deep Lake UI

GTSRB dataset

What is GTSRB Dataset?

The German Traffic Sign Recognition Benchmark (GTSRB) includes 43 different types of traffic signs, divided into 39,209 training and 12,630 test pictures. The photographs feature a variety of lighting and settings.

Download GTSRB Dataset in Python

Instead of downloading the GTSRB dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load GTSRB Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/gtsrb-train")

Load GTSRB Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/gtsrb-test")

GTSRB Dataset Structure

GTSRB Data Fields

images: tensor representing the image in jpg format.
boxes: tensor representing bounding box around the traffic sign signal.
labels: tensor to represent the category of the signal.
shapes: tensor to identify the shape of the signal board.
colors: tensor to identify the color of the sign board.

GTSRB Data Splits

The GTSRB dataset training set is composed of 39209.
The GTSRB dataset testing set is composed of 7357.

How to use GTSRB Dataset with PyTorch and TensorFlow in Python

Train a model on GTSRB dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on the GTSRB dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about GTSRB Dataset

GTSRB Dataset Description

Homepage: https://benchmark.ini.rub.de/
Repository: https://github.com/surmenok/GTSRB
Paper: Sebastian Houben and Johannes Stallkamp and Jan Salmen and Marc Schlipsing and Christian Igel in Detection of Traffic Signs in Real-World Images: The {G}erman {T}raffic {S}ign {D}etection {B}enchmark
Point of Contact: N/A

GTSRB Dataset Curators

Sebastian Houben and Johannes Stallkamp and Jan Salmen and Marc Schlipsing and Christian Igel

GTSRB Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

GTSRB Dataset Citation Information

				
					@inproceedings{Houben-IJCNN-2013,
   author = {Sebastian Houben and Johannes Stallkamp and Jan Salmen and Marc Schlipsing and Christian Igel},
   booktitle = {International Joint Conference on Neural Networks},
   title = {Detection of Traffic Signs in Real-World Images: The {G}erman {T}raffic {S}ign {D}etection {B}enchmark},
   number = {1288},
   year = {2013},
}

GTSRB Dataset FAQs

What is the GTSRB dataset for Python?

The German Traffic Sign Recognition dataset is large, organized, open-source, and annotated. It is often used for developing classification machine learning models. In the dataset, although the actual traffic sign is not necessarily a square, or centered, the dataset comes with an annotation file that specifies the bounding boxes for each traffic sign.

How to download the GTSRB dataset in Python?

You can load the GTSRB dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load the GTSRB dataset training subset and testing subset in Python.

How can I use the GTSRB dataset in PyTorch or TensorFlow?

You can stream the GTSRB dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on the GTSRB dataset with PyTorch in Python or train a model on the GTSRB dataset with TensorFlow in Python.