Omkar2118/datasets huggingface dataset:
KITTI-Depth

The **KITTI-Depth** dataset includes depth maps from projected LiDAR point clouds that were matched against the depth estimation from the stereo cameras. The depth images are highly sparse with only 5% of the pixels available and the rest is missing. The dataset has 86k training images, 7k validation images, and 1k test set images on the benchmark server with no access to the ground truth.

Source: [Confidence Propagation through CNNs for Guided Sparse Depth Regression](https://arxiv.org/abs/1811.01791)
Image Source: [http://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction](http://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction)

Introducing paper: Sparsity Invariant CNNs
SoF
Full name: Specs on Faces

The **Specs on Faces** (**SoF**) dataset, a collection of 42,592 (2,662×16) images for 112 persons (66 males and 46 females) who wear glasses under different illumination conditions. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is focused on two challenges: harsh illumination environments and face occlusions, which highly affect face detection, recognition, and classification. The glasses are the common natural occlusion in all images of the dataset. However, there are two more synthetic occlusions (nose and mouth) added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty (easy, medium, and hard). That enlarges the number of images to be 42,592 images (26,112 male images and 16,480 female images). There is metadata for each image that contains many information such as: the subject ID, facial landmarks, face and glasses rectangles, gender and age labels, year that the photo was taken, facial emotion, glasses type, and more.

Source: [https://sites.google.com/view/sof-dataset](https://sites.google.com/view/sof-dataset)
Image Source: [https://sites.google.com/view/sof-dataset](https://sites.google.com/view/sof-dataset)

Introducing paper: AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces
KAIST Urban

This data set provides Light Detection and Ranging (LiDAR) data and stereo image with various position sensors targeting a highly complex urban environment. The presented data set captures features in urban environments (e.g. metropolis areas, complex buildings and residential areas). The data of 2D and 3D LiDAR are provided, which are typical types of LiDAR sensors. Raw sensor data for vehicle navigation is presented in a file format. For convenience, development tools are provided in the Robot Operating System (ROS) environment.

Source: [https://sites.google.com/view/complex-urban-dataset](https://sites.google.com/view/complex-urban-dataset)
Image Source: [https://irap.kaist.ac.kr/dataset/](https://irap.kaist.ac.kr/dataset/)

Introducing paper: Complex urban dataset with multi-level sensors from highly diverse urban environments