Falls are a major health problem with one in three people over the age of 65 falling each year, oftentimes causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data. Fall detection algorithms are typically trained on simulated fall data that contain a well-balanced number of examples of falls and activities of daily living. However, real-world falls occur infrequently, making them difficult to capture and causing severe data imbalance.


Follow instruction in readme file


The detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), Hewlett Packard Enterprise, SolarAid, and Data Science Experts, aims to promote research in automatic detection of human settlements deprived of access to electricity using multimodal and multitemporal remote sensing data.

Last Updated On: 
Sun, 02/28/2021 - 07:59
Citation Author(s): 
Colin Prieur, Hana Malha, Frederic Ciesielski, Paul Vandame, Giorgio Licciardi, Jocelyn Chanussot, Pedram Ghamisi, Ronny Hänsch, Naoto Yokoya

Dataset for the meta-heuristics scheduling algorithm


A medium-scale synthetic 4D Light Field video dataset for depth (disparity) estimation. From the open-source movie Sintel. The dataset consists of 24 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20–50 frames, and has ground-truth disparity values, so that can be used for training deep learning-based methods. Each scene was rendered with a clean pass after modifying the production file of Sintel with reference to the MPI Sintel dataset.



Light Field videos:
  • 24 synthetic scenes
  • 1,204x436 pixels
  • 9x9 views
  • 20--50 frames
Ground-truth disparity values:
  • Provides disparity values for all scenes, all views, all frames, and all pixels.
  • The disparity value was obtained by transforming the depth value obtained in Blender.
    • The unit of disparity is [mm], so if the unit of [px] is needed, it needs to be multiplied by 32 to convert. (Mentioned in this issue)
Light Field setup:
  • Rendering with a “clean” pass using Blender (render25 branch).
  • The Light Field was captured by moving the camera to 9x9 viewpoints with a baseline of 0.01[m] towards a common focal plane while keeping the optical axes parallel. 



Three types of datasets are provided on this page.

The reason for the three types is to eliminate the need to download extra data.

All types include all scene, all frames, and differ only in the RGB and disparity views.

1. Sintel_LFV_9x9_with_all_disp.zip
  • Includes RGB sequences for 9x9 views and disparity sequences for 9x9 views.
  • The unzipped file has 190GiB.
  • It can be used for a variety of depth estimations, e.g. not only light field but also (multi) stereo, as it includes the disparity for all views.
2. Sintel_LFV_9x9.zip
  • Includes RGB sequences for 9x9 views and disparity sequences for center view.
  • The unzipped file has 51.4GiB.
  • It can be used for light field-based depth estimations using 9x9 views.
3. Sintel_LFV_cross-hair.zip
  • Includes RGB sequences for cross-hair views and disparity sequences for center view.
  • The unzipped file has 12.1GiB.
  • It can be used for light field-based depth estimations using cross-hair views.
    • This is the data we used in our paper. (Note: We didn't use the scene named shaman_b_2 because it was not completed at that time.)

* The datasets contain RGB in .png and disparity in .npy.


File structure.

The following is the case of Sintel_LFV_9x9_with_all_disp.

In other cases, there is no view directory or no disparity file.

The naming convention for the view directory is {viewpoint_y:02}_{viewpoint_x:02} with 00_00 being the upper left viewpoint.


  ┣━━ ambushfight_1/    ...    scene directory
  ┃          ┣━━ 00_00/ ...    view directory
  ┃          ┃         ┣━━ 000.png ...    RGB of frame 0
  ┃          ┃         ┣━━ 000.npy ...    disparity of frame 0
  ┃          ┃         ┣━━ 001.png ...    RGB of frame 1
  ┃          ┃         ┣━━ 001.npy ...    disparity of frame 1
  ┃          ┣━━ 04_04/ ...    center view directory 
  ┃          ┃         ┣━━ 000.png ...    RGB of frame 0
  ┃          ┃         ┣━━ 000.npy ...    disparity of frame 0
  ┃          ┗━━ .../
  ┣━━ ambushfight_2/
  ┣━━ ambushfight_3/
  ┗━━ .../




  • Data is now available to registered users.
  • Registration closed on March 1, 2021.


Last Updated On: 
Tue, 03/02/2021 - 15:56

This dataset is composed of side channel information (e.g., temperatures, voltages, utilization rates) from computing systems executing benign and malicious code.  The intent of the dataset is to allow aritificial intelligence tools to be applied to malware detection using side channel information.


Retinal Fundus Multi-disease Image Dataset (RFMiD) consisting of a wide variety of pathological conditions. 


Detailed instructions about this dataset are available on the challenge website: https://riadd.grand-challenge.org/.


Predicting energy consumption is currently a key challenge for the energy industry as a whole.  Predicting the consumption in a certain area is massively complicated due to the sudden changes in the way that energy is being consumed and generated at the current point in time. However, this prediction becomes extremely necessary to minimise costs and to enable adjusting (automatically) the production of energy and better balance the load between different energy sources.

Last Updated On: 
Wed, 12/23/2020 - 12:16
Citation Author(s): 
Isaac Triguero

This dataset contains 1944 data, which are scanned by the HIS-RING PACT system.

the data sampling rate of our system is 40 MSa/s, a 128-elements 2.5MHz full-view ring-shaped transducer with 30mm radius. 

 continuous updating.....


本研究中使用的柑橘叶数据集来自 PlantVillage [24],用于以下方面的开放访问公共资源: 与农业有关的内容。数据集包括三种类型柑桔叶片:柑桔健康,柑桔HLB(黄龙病) 一般,柑橘HLB严重。原始数据集包含4577张柑橘叶片图像,分为三部分 分类