We chose 8 publicly available CT volumes of COVID-19 positive patients which were available from https://doi.org/10.5281/zenodo.3757476 and used 3D slicer to generate volumetric annotations of 512*512 dimension for 5 lung lobes namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe. These annotations are validated by a radiologist with over 15 years of experience. 


CT volumes can be downloaded from https://doi.org/10.5281/zenodo.3757476

Volumetric annotations for 5 lobe segments namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe are saved as segments 1 to 5 respectively. 

For scans with prefix coronacases_00x their corresponding annotations are uploaded with suffix lobes

The scans and annotations measure 512*512 and are in .nii format


The simulated InSAR building dataset contains 312 simulated SAR image pairs generated from 39 different building models. Each building model is simulated at 8 viewing-angles. The sample number is 216 of the train set and is 96 of the test set. Each simulated InSAR sample contains three channels: master SAR image, slave SAR image, and interferometric phase image. This dataset serves the CVCMFF Net for building semantic segmentation of InSAR images.


The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks.


This repository contains the AURORA dataset, a multi sensor dataset for robotic ocean exploration.

It is accompanied by the report "AURORA, A multi sensor dataset for robotic ocean exploration", by Marco Bernardi, Brett Hosking, Chiara Petrioli, Brian J. Bett, Daniel Jones, Veerle Huvenne, Rachel Marlow, Maaten Furlong, Steve McPhail and Andrea Munafo.

Exemplar python code is provided at https://github.com/noc-mars/aurora.


The dataset provided in this repository includes data collected during cruise James Cook 125 (JC125) of the National Oceanography Centre, using the Autonomous Underwater Vehicle Autosub 6000. It is composed of two AUV missions: M86 and M86.

  • M86 contains a sample of multi-beam echosounder data in .all format. It also contains CTD and navigation data in .csv format.

  • M87 contains a sample of the camera and side-scan sonar data. The camera data contains 8 of 45320 images of the original dataset. The camera data are provided in .raw format (pixels are ordered in Bayer format). The size of each image is of size 2448x2048. The side-scan sonar folder contains a one ping sample of side-scan data provided in .xtf format.

  • The AUV navigation file is provided as part of the data available in each mission in .csv form.


Results(including reported and extra results) for LSstab. Please refer to our paper "Efficient real-time video stabilization with a novel least squares formulation and parallel AC-RANSAC".


Stabilization results for LSstab. Please refer to our paper"Efficient real-time video stabilization with a novel

least squares formulation and parallel AC-RANSAC"


Stabilization results include:

(1) stabilized videos reported in the paper

(2) extra stabilized videos

(3) Challenging videos that LStab fails to stabilize. 


This is a dataset of diabetic foot. The dataset was collected by Lifang Liu from Shanghai Municipal Eighth People's Hospital. The dataset contains 1211 images composed 507 DF images and 704 non-DF images. These DF images contain all Wagner grade types of DF. The non-DF images mainly contain other chronic wounds from the feet and legs, such as acne, pressure ulcer and venous embolism of lower extremity.


Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth.



When using this dataset, please cite our corresponding paper:

Maximilian Schambach and Michael Heizmann:

"A Multispectral Light Field Dataset and Framework for Light Field Deep Learning"

IEEE Access, 2020

DOI: 10.1109/ACCESS.2020.3033056



The dataset consists of 500 randomly generated scenes as well as 7 hand-crafted scenes for detailed performance evaluation. 

The scenes are rendered as multispectral light fields of shape (11, 11, 512, 512, 13) with depth and disparity ground truth of every subaperture view.

The lightfields are provided with 16bit uint precision, the depth and disparities with 32bit float precision.

The scenes are rendered in two different camera configurations: one corresponding to a light field camera in the unfocused design (plenoptic 1.0) with a focused main lens (annotated with "F") and one where the main lens is focussed at infinity (annotated with "INF") )which is equivalent to a camera array with parallel optical axes. In the "F" configuration, disparities range from ca. -2.5px to 3px where a disparity of 0px corresponds to the focal plane. In the "INF" configuration, the focus is set to infinity, hence all disparities are positive.

We provide the raw rendered data, the abstract source files (so rendering of additional ground truth is possible) as well as multiple pre-patch and converted versions.


Dataset Content

We provide the following dataset files for downloading:



Contains the complete RAW rendered data (both the F and INF camera configuration), including the multispectral light fields in the ENVI format, traced depth maps and converted disparity maps for every subaperture view in the PFM format. 

The light fields are of shape (11, 11, 512, 512, 13), the depth and disparity of shape (11, 11, 512, 512, 1) and saved as 2D images in the Subaperture Image view. 

To load the light fields and disparities, you may use our Python library plenpy.

To patch the RAW data into a .h5 dataset, see the provided Python scripts contained in SCRIPTS.zip . 

We provide pre-patched versions of the dataset (see below). If the pre-patched version do not fit your needs (e.g. you need a different spatial resolution), use our provided patch script.

The patched .h5 data can then directly be used with our deep learning framework LFCNN.



Contain the hand-crafted dataset challenges. Includes the RAW rendered data, as well as conversions to Numpy's .npy format in the case of (11, 11) and (9,9) angular resolution.

Furhter contains composed h5 files to be used directly with our deep learning framework LFCNN.



Identical to CHALLENGES_MULTISPECTRAL but converted to RGB.


Pre-patched data


Multispectral dataset in the "F" configuration, patched to (11, 11, 36, 36, 13) light field patches with the corrresponding disparity map of the central view.


Multispectral dataset in the "F" configuration, patched to (9, 9, 36, 36, 13) light field patches with the corrresponding disparity map of the central view.


Same as previous, but in the "INF" camera configuration.


Same as DATASET_MULTISPECTRAL_PATCHED_F_9x9_36x36.zip but converted to RGB.


Same as DATASET_MULTISPECTRAL_PATCHED_INF_9x9_36x36.zip but converted to RGB.


The SCRIPTS.zip file contains a script to convert a set of light fields to a patched set saved in the .h5 format. 

Use these scripts to patch the raw dataset into light field patches of a self-defined shape.

See the scsript for comments on usage.


ALL-IDB (Acute Lymphoblastic Leukemia) Image Database for Image Processing

ALL-IDB dataset comprises of two subsets among them one subset has 260 segmented lymphocytes of them 130 belongs to the leukaemia and the remaining 130 belongs to the non leukaemuia class it requires only classification. second subset has around 108 non segmented blood images that belongs to the leukaemia and non leukaemia groups thus requires segmentation and classification.




Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately.


Annotated image dataset of household objects from the RoboFEI@Home team

This data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.

The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.


This data set contains two separate sets of annotated images. Common features of the image sets:

  • Images are saved in JPG format
  • Annotations are made with labelImg
  • Both sets contain videos in MP4 format to test trained detection models

Set 1

166 annotated images with 1028 objects of the following 13 classes:

  1. cereal
  2. chocolate_milk
  3. heineken
  4. iron_man
  5. medicine
  6. milk_bottle
  7. milk_box
  8. monster
  9. purple_juice
  10. red_juice
  11. shampoo
  12. tea_box
  13. yellow_juice

There are also 28 videos for testing, shot with multiple smartphones.

Set 2

388 annotated images with 1737 objects of the following 20 classes:

  1. apple
  2. basket
  3. cereal
  4. chocolate_drink
  5. cloth_opl
  6. coke
  7. crackers
  8. grape_juice
  9. help_me_carry_opl
  10. noodles
  11. orange
  12. orange_juice
  13. paprika
  14. potato_chips
  15. pringles
  16. sausages
  17. scrubby
  18. sponge_opl
  19. sprite
  20. tray

There is also a single long video and 398 unannotated images for testing.


The data uploaded here shall support the paper 

Decision Tree Analysis of  ...

which has been submitted to IEEE Transactions on Medical Imaging (2020, September 25) by the authors

Julian Mattes, Wolfgang Fenz, Stefan Thumfart, Gerhard Haitchi, Pierre Schmit, Franz A. Fellner

During review the data shall only be visible for the reviewers of this paper. Afterwards this abstract will be modified and complemented and a dataset image will be uploaded.