Code and Data
This dataset contains 19383 image sequences for lane detection, and 39460 frames of them are labeled. These images were divided into two parts, a training dataset contains 9548 labeled images and augmented by four times, and a test dataset has 1268 labeled images. The size of images in this dataset is 128*256.
A number of 118 subjects are involved in the data collection. Among them, 20 subjects collect a larger amount of data in two days, with each has thousands of samples, and 98 subjects collect a smaller amount of data in one day, with each has hundreds of samples. Each data sample contains the 3-axis accelerometer data and the 3-axis gyroscope data. The sampling rate of all sensor data is 50 Hz. According to the different evaluation purposes, we construct six datasets based on the collected data.
DeepCrack consists of four datasets: CrackTree260 dataset, CRKWH100 dataset, CrackLS315 dataset, Stone331 dataset.
We introduce a pairwise quantifiedsimilarity calculated on the normalized semantic labels. Based on this, we divide the pairwise similarity into two situations ‘hardsimilarity’ and ‘soft similarity’, where cross-entropy loss andmean square error loss are adapted respectively for more robustfeature learning and hash coding .
This dataset contains 660 Flying-Apsaras painting images from Mogao Grottoes in Dunhuang, China. These images were labeled into three categories according to the eras of the Flying-Apsaras art they were created – 220 images from the infancy period of the Flying-Apsaras art (421–556), 220 images from the creative period of the Flying-Apsaras art (557–618), and 220 images from the mature period of the Flying-Apsaras art (619–959).