Code and Data



tvtLANE [datasets][codes]

multinatgeom image 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.

whuGAIT [datasets][codes]

matdb image 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 [datasets][codes]

defog image DeepCrack consists of four datasets: CrackTree260 dataset, CRKWH100 dataset, CrackLS315 dataset, Stone331 dataset.

IDHN [codes]

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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 .

Dunhuang660 [datasets]

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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).