-
Cityscapes
The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and... -
KITTI dataset
The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding... -
Microsoft COCO
The Microsoft COCO dataset was used for training and evaluating the CNNs because it has become a standard benchmark for testing algorithms aimed at scene understanding and... -
ImageNet Large Scale Visual Recognition Challenge
A benchmark for low-shot recognition was proposed by Hariharan & Girshick (2017) and consists of a representation learning phase without access to the low-shot classes and a... -
CityPersons
The dataset used for pedestrian detection, occlusion and body part relevance assessment. -
KITTI 2015
The KITTI 2015 dataset is a real-world dataset of street views, containing 200 training stereo image pairs with sparsely labeled disparity from LiDAR data. -
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
Joint-DetNAS is a unified framework for object detection that jointly optimizes NAS, pruning, and dynamic distillation. -
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families
Transformer-based detectors (DETRs) are becoming popular for their simple framework, but the large model size and heavy time consumption hinder their deployment in the real world. -
ImageNet-1k
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used it for language modeling and image classification tasks.
