Accurate fire load (combustible objects) information is crucial for safety design and resilience assessment of buildings. Traditional fire load acquisition methods, such as fire load survey, which are time-consuming, tedious, and error-prone, failed to adapt to dynamic changed indoor scenes. As a starting point of automatic fire load estimation, fast recognition and detection of indoor fire load are important. Thus, A dataset containing images of indoor scenes and annotations of instance segmentation is developed in this research.

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We created a 2563-image custom dragon fruit image dataset, with 1248 images of raw dragon fruits and 1315 photographs of ripe dragon fruits. The images were taken with the Nikon D5200 DSLR and OnePlus 6's Sony IMX 519 16 megapixel camera. The photographs taken with the DSLR camera had a resolution of 4000 by 6000 pixels, while those taken with the OnePlus6 had a resolution of 3456 by 4608 pixels. They were photographed in natural sunlight. The average temperature during that time was 28°C (84.2°F), with partly sunny skies, 65 percent humidity, and 17 km/h wind speeds.

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The widespread use of information technology in several facets of contemporary life has resulted in the consideration of vehicles as conceptual objects in information systems. The primary requirements in traffic monitoring and control are automated toll collection, vehicle detection, and automatic number plate detection and recognition. An informative dataset on Indian vehicles is necessary to accomplish this in Indian region.

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Two in-air signature databases were created. Forty participants voluntarily took part in each of the two databases’ construction. Some of them participated in both databases construction. Each participant signs in the air five signatures and imitates five signatures of five other participants.

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Abstract

This synthetic dataset or phantom consists of 6 raw format databases, in the three–dimensional (3-D) domain, which are identified as follows:

DB1: Ground Truth

DB2: Poisson noise

DB3: Stair-step artifact

DB4: Streak artifact

DB5: Both artifacts

DB6: Hybrid.

 

 

 

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Akshi IMAGE, a new Indian ethnicity retinal fundus image database, has been established for the evaluation of computer-assisted glaucoma prescreening methods. ‘Akshi’ is a Sanskrit word for the ‘Eye’ and ‘IMAGE’ is an acronym for IISc-MAHE Glaucoma Evaluation database. This database is a result of an interdisciplinary collaboration between Indian Institute of Science (IISc) and Manipal Academy of Higher Education (MAHE). The database consists of retinal color fundus images acquired using three different devices.

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This is a dataset about clustered pop-pepper in Guiyang, China. Some of our depth data are distorted, but generally available. It will be continuously updated in the future.

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The C3I Synthetic Human Dataset provides 48 female and 84 male synthetic 3D humans in fbx format generated from iClone 7 Character creator “Realistic Human 100” toolkit with variations in ethnicity, gender, race, age, and clothing. For each of these, it further provides the full-body model with five different facial expressions – Neutral, Angry, Sad, Happy, and Scared. Along with the body models, it also open-sources a data generation pipeline written in python to bring those models into a 3D Computer Graphics tool called Blender.

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Dataset for segmentation of the defects on the surfaces of the military cartridge cases. The datasets with non-defective, defective and masked image classes of the defective cartridge cases.

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This open dataset is subject to CC BY-NC-SA 4.0 License. The dataset is intended for scientific research purposes and it cannot be used for commercial purposes. The authors encourage users to use it for public research and as a testbench for private research. Please note that any promotional/marketing material built upon this dataset should be backed by publicly available description of the work leading to the promotional/marketing claims.

Instructions: 

This dataset contains different types of data:

  • Subject's basic information (single .csv file)
  • Body measurements (single .csv file)
  • Anonymous 3D avatars (one .obj file per avatar)

Please read Readme.pdf file for full details of the content.

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