This dataset contains sheets of handwritten Telugu characters separated in boxes. It contains vowel, consonant, vowel-consonant and consonant-consonant pairs of Telugu characters. The purpose of this dataset is to act as a benchmark for Telugu handwritting related tasks like character recognition. There are 11 sheet layouts that produce 937 unique Telugu characters. Eighty three writers participated in generating the dataset and contributed 913 sheets in all. Each sheet layout contains 90 characters except the last which contains 83 characters where the last 10 are english numerals 0-9.


A paradigm dataset is constantly required for any characterization framework. As far as we could possibly know, no paradigmdataset exists for manually written characters of Telugu Aksharaalu content in open space until now. Telugu content (Telugu: తెలుగు లిపి, romanized: Telugu lipi), an abugida from the Brahmic group of contents, is utilized to compose the Telugu language, a Dravidian language spoken in the India of Andhra Pradesh and Telangana just a few other neighboring states. The Telugu content is generally utilized for composing Sanskrit writings.


A benchmark dataset is always required for any classification or recognition system. To the best of our knowledge, no benchmark dataset exists for handwritten character recognition of Manipuri Meetei-Mayek script in public domain so far. Manipuri, also referred to as Meeteilon or sometimes Meiteilon, is a Sino-Tibetan language and also one of the Eight Scheduled languages of Indian Constitution. It is the official language and lingua franca of the southeastern Himalayan state of Manipur, in northeastern India.


Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.


The MEx Multi-modal Exercise dataset contains data of 7 different physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers, a pressure mat and a depth camera.


The dataset can be used for exercise recognition, exercise quality assessment and exercise counting, by developing algorithms for pre-processing, feature extraction, multi-modal sensor fusion, segmentation and classification.


Data collection method

Each subject was given a sheet of 7 exercises with instructions to perform the exercise at the beginning of the session. At the beginning of each exercise the researcher demonstrated the exercise to the subject, then the subject performed the exercise for maximum 60 seconds while being recorded with four sensors. During the recording, the researcher did not give any advice or kept count or time to enforce a rhythm.



Obbrec Astra Depth Camera 

-       sampling frequency – 15Hz 

-       frame size – 240x320


Sensing Tex Pressure Mat

-       sampling frequency – 15Hz

-       frame size – 32*16

Axivity AX3 3-Axis Logging Accelerometer

-       sampling frequency – 100Hz

-       range – 8g


Sensor Placement

All the exercises were performed lying down on the mat while the subject wearing two accelerometers on the wrist and the thigh. The depth camera was placed above the subject facing down-words recording an aerial view. Top of the depth camera frame was aligned with the top of the pressure mat frame and the subject’s shoulders such that the face will not be included in the depth camera video.


Data folder

MEx folder has four folders, one for each sensor. Inside each sensor folder,

30 folders can be found, one for each subject. In each subject folder, 8 files can be found for each exercise with 2 files for exercise 4 as it is performed on two sides. (The user 22 will only have 7 files as they performed the exercise 4 on only one side.)  One line in the data files correspond to one timestamped and sensory data.


Attribute Information


The 4 columns in the act and acw files is organized as follows:

1 – timestamp

2 – x value

3 – y value

4 – z value

Min value = -8

Max value = +8


The 513 columns in the pm file is organized as follows:

1 - timestamp

2-513 – pressure mat data frame (32x16)

Min value – 0

Max value – 1


The 193 columns in the dc file is organized as follows:

1 - timestamp

2-193 – depth camera data frame (12x16)


dc data frame is scaled down from 240x320 to 12x16 using the OpenCV resize algorithm

Min value – 0

Max value – 1


Simulated Disaster Victim dataset consists of images and video frames containing simulated human victims in cluttered scenes along with pixel-level annotated skin maps. The simulation was carried out in a controlled environment with due consideration towards the health of all the volunteers. To generate a real effect of a disaster, Fuller’s earth is used which is skin-friendly and does not cause harm to humans. It created an effect of disaster dust over the victims in different situations. The victims included one female and four male volunteers.


CSIR-CSIO Simulated Disaster Victim Dataset

This dataset was collected as part of research work on locating victims in catastrophic situations in different poses, occlusions and varied illumination conditions of simulated victims in images and video. The work and dataset is explained in paper “Data-driven Skin Detection in Cluttered Search & Rescue Environments” and Ph.D. thesis titled “Automated Detection of Disaster Victims in Cluttered Environments”. The dataset is divided in two parts: (a) SDV1 containing simulated disaster victim images with corresponding ground truth files, and (b) SDV2 dataset consisting of 15 video clips of simulated disaster victims.

 SDV1 dataset:

·        128 images (768x509) with 128 ground truth binary maps.

·        Five volunteers as victims (one female and four male).

SDV2 dataset:

·        15 video clips consisting of 6315 frames and 557 pixel level annotations of skin maps.

·        Each frame has a resolution of 960x540.

·        Ground truth binary maps available for random frame number in each sequence.

·        Five volunteers as victims (one female and four male).

 Note: Folder name ‘GT’ correspond to the ground truth folder



·The research work was funded by Council of Scientific & Industrial Research (CSIR) and was carried out at CSIR-Central Scientific Instruments Organisation, Chandigarh. The research work was also supported by the Academy of Scientific & Innovation Research (AcSIR), India.

This dataset is the property of CSIR-CSIO, Chandigarh, India.

·It is to be used only for research purpose giving due credit by citation.


This dataset is introduced in paper "Point-cloud-based place recognition using CNN feature extraction" by T. Sun, et al.

This dataset is for place recognition.

The dataset is collected using an omni stereo camera and a VLP-16 Velodyne LiDAR tied together and placed on a tripod.


Drive testing is a common practice by mobile operators to evaluate the performance and coverage of their deployed mobile communication systems. Drive testing is, however, a very expensive practice. Furthermore, due to the complexity of future 5G mobile networks, accurate and efficient ways of optimizing and evaluating coverage are needed. The dataset contains measurements from a deployed LTE-A mobile communication system and corresponding satellite images.


The feature matrix



Contains the following features:

  • Longitude
  • Latitude
  • Speed (in km/h)
  • Distance (in km)
  • Distance_x
  • Distance_y
  • PCI (Integer)
  • PCI_64 (811 MHz base station) (one-hot encoding)
  • PCI_65 (811 MHz base station) (one-hot encoding)
  • PCI_302 (2630 MHz base station) (one-hot encoding)


The output matrix


  • SINR (dB)
  • RSRP (dBm)
  • RSRQ (dB)
  • Power/RSSI (dBm)


Each row corresponds to an image in mapbox_api/*.png



Starfish color image from Berkely dataset.


Glaucoma is the leading cause of irreversible blindness in the world, and primary angle closure glaucoma (PACG) is one of the main subtypes. PACG patients have narrow chamber angle and can be diagnosed by goniscopy, which may cause discomfort and relies too much on personal experience. Anterior segment OCT is able to provide 3D scan of the anterior chamber and assist the ophthalmologists evaluate the condition of chamber angle. It’s faster and objective compare with goniscopy.


Welcome to the Retinal Fundus Glaucoma Challenge! REFUGE was organized as a half day Challenge in conjunction with the 5th MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), a Satellite Event of the MICCAI 2018 conference in Granada, Spain. The goal of the challenge is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images. With this challenge, we made available a large dataset of 1200 annotated retinal fundus images.