Beijing Building Dataset(BGB) is an elevation satellite image dataset which is integrated by satellite image and aerial photograph for building detection and identification. It contains 2000 images from Google Earth History Map of five different areas in Beijing on November 24th, 2016, and all these images are 512*512 in resolution ratio with a precision of 0.458m. It covers more than 100 km2 geographic areas of Beijing both in suburbs and urban areas.


In recent years, the utilization of biometric information has become more and more common for various forms of identity verification and user authentication. However, as a consequence of the widespread use and storage of biometric information, concerns regarding sensitive information leakage and the protection of users' privacy have been raised. Recent research efforts targeted these concerns by proposing the Semi-Adversarial Networks (SAN) framework for imparting gender privacy to face images.


Double-identity fingerprint is a fake fingerprint created by aligning two fingerprints for maximum ridge similarity and then joining them along an estimated cutline such that relevant features of both fingerprints

are present on either sides of the cutline. The fake fingerprint containing the features of the criminal and his innocuous accomplice can be enrolled with an electronic machine readable travel document and later used to cross the automated


Semantic Segmentation Image


A composite dataset with eight videos (totaling the pronunciation of seventeen words, with intervals, sagittal plane, and gray scale), for experiments in computer vision, video processing, and articulation investigation of the vocal tract.


In this dataset:- There is no audio.- Sagittal image- Grey Scale


Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. This dataset contains 388 images of structures with and without dirt buildup.

One can use this dataset for experimentation on classifying the dirt buildup.


The data are separated into folders that specify each class of the dataset: Clean and Dirty.


This archive contains images and labels for the Idly-Dosa-Vada (IDV) dataset, for use with Yolo (and Tensorflow) object detection frameworks.


This archive contains images and labels for the Idly-Dosa-Vada (IDV) dataset, for use with Yolo (and Tensorflow) object detection frameworks.

The dataset contains 1009 images, and corresponding labels.

The dataset was created by using euclidaug, using only 6 images per class.


Folder structure after extracting

out_images - contains all training images

out_labels - contains labels for each image, in Yolo format


For usage, refer to the IEEE-DL-TAP instructions, which are derived from


Step 1: Generate full list of image files, for use in the training process. In Windows, this is done using the below command:


dir /s/b *.jpg > trainingfile.txt


Step 2: Using the above file, and the labelled images and labels, start the training process with Yolo using instructions at


Step 3: Perform inference using Yolo


Understanding causes and effects in mechanical systems is an essential component of reasoning in the physical world. This work poses a new problem of counterfactual learning of object mechanics from visual input. We develop the COPHY benchmark to assess the capacity of the state-of-the-art models for causal physical reasoning in a synthetic 3D environment and propose a model for learning the physical dynamics in a counterfactual setting.


Pedestrian detection has never been an easy task for computer vision and automotive industry. Systems like the advanced driver assistance system (ADAS) highly rely on far infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what is the performance in adverse weather conditions.


Prefix _b - means benchmark, otherwise used for training/testing


Each recording folder contains:

  16BitFrames - 16bit original capture without processing.

  16BitTransformed - 16bit capture with low pass filter applied and scaled to 640x480.

  annotations - annotations and 8bit images made from 16BitTransformed.

  carParams.csv - a CAN details with coresponding frame ID.

  weather.txt - weather information in which the recording was made.


Annotations are made in YOLO (You only look once) Darknet format.


To have images without low pass filter applied you should make the following steps:

- Take 16bit images from 16BitFrames folder and open with OpenCV function like: Mat input = imread(<image_full_path>, -1);

- Then use convertTo function like: input.convertTo(output, input.depth(), sc, sh), where output is transformed Mat, sc is scale and sh is shift from carParams.csv file.

- Finally, scale image to 640x480 


ADAM is organized as a half day Challenge, a Satellite Event of the ISBI 2020 conference in Iowa City, Iowa, USA.