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Football360 – Introducing a New Dataset for Camera Calibration in Sports Domain


The aim of this dataset is to aid the development and evaluation of computer vision algorithms, primarily the radial distortion correction algorithms, in the sports domain.

This dataset contains 268 panorama images, and was created using the PANONO panoramic camera in 3 football arenas in Slovakia. Each arena was covered from numerous locations on all levels of the tribune, and broadcast camera platforms. The images capture regular football game, pitch maintenance, low/challenging lighting conditions, day and night situations.

ArenaNumber of images
Dunajská Streda53

You can download the raw images from the following link: football360-raw.tar.gz (7.7 GB)

There is also an official GitHub repository available at:


Raw Images

Raw images are stored as 16384×8192 JPG, they are the direct result of the PANONO stitching service.


Exported Sets

There are four exported datasets available for direct download and use. Each dataset contains a collection of images (data) distorted with known distortion coefficients (labels).

Set namePurposeImagesPresetDownload Link
ATraining30,000setA.jsonfootball360-setA.h5 (10.5 GB)
BTraining100,000setB.jsonfootball360-setB.h5 (35.2 GB)
CTraining300,000setC.jsonfootball360-setC.h5 (105.5 GB)
VValidation10,000setV.jsonfootball360-setV.h5 (3.5 GB)

You can see an example of how to load and use these files in a linked notebook –

Distorted ImageCorrected Image


Igor Janos

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Explainable 3D convolutional neural network using GMM encoding

Abstract:     The aim of this paper is to propose a novel method to explain, interpret, and support the decision-making process of deep Convolutional Neural Network (CNN). This is achieved by analyzing neuron activations of trained 3D-CNN on selected layers via Gaussian Mixture Model (GMM)   …  more


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Automatic Music Transcription using WaveNet

Deep generative models such as WaveNet are surprisingly good at various modelling tasks. We exploit the modelling capacity of WaveNet architecture in a setup that is quite different from the original generative case: for feature extraction and pattern recognition in sake of polyphonic music transcription. The model is trained end-to-end to perform the underlying task of multiple fundamental frequency estimation by processing raw waveforms of digital audio signal.   …  more