44 datasets found

Groups: Object Detection Formats: JSON

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  • PASCAL Context

    The PASCAL Context dataset is a benchmark for multi-task learning in computer vision. It contains 10103 images with 5 tasks: semantic segmentation, human body part segmentation,...

  • Confluence: A Robust Non-IOU Alternative to Non-Maxima Suppression in Object ...

    Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection.

  • KITTI 2012

    KITTI 2012 is a real-world dataset in the outdoor scenario, and contains 194 training and 195 testing stereo image pairs with the size of 376 × 1240.

  • CARLA

    The CARLA dataset is a complex urban-like environment with multi-agent dynamics, pedestrians, intersections, cross-traffic, roundabout, and changing weather conditions.

  • Scannet

    The dataset used for training and testing the proposed RGBD-based obstacle avoidance system for visually impaired people.

  • Mask R-CNN

    The dataset used in this paper for training and testing the KAISA optimizer framework.

  • PASCAL VOC 2007

    Multi-label image recognition is a practical and challenging task compared to single-label image classification.

  • KITTI Vision Benchmark Suite

    The KITTI Vision Benchmark Suite is a dataset used for object detection and tracking in autonomous vehicles.

  • YOLOv2

    The dataset used in the paper is a publicly available dataset for object detection.

  • NYUv2

    Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance...

  • YFCC100M

    The dataset used in the paper is YFCC100M, a large-scale video dataset. The dataset is used for foreground and background patch extraction and object recognition tasks.

  • SemanticPOSS

    A point cloud dataset with large quantity of dynamic instances, consisting of 2,988 real-world scans with point-level annotations.

  • Pascal VOC

    Semantic segmentation is a crucial and challenging task for image understanding. It aims to predict a dense labeling map for the input image, which assigns each pixel a unique...

  • SUN RGB-D

    RGB-D scene recognition approaches often train two standalone backbones for RGB and depth modalities with the same Places or ImageNet pre-training. However, the pre-trained...

  • ImageNet Dataset

    Object recognition is arguably the most important problem at the heart of computer vision. Recently, Barbu et al. introduced a dataset called ObjectNet which includes objects in...

  • NeRF

    NeRF [33] has demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray...

  • MS-COCO

    Large scale datasets [18, 17, 27, 6] boosted text conditional image generation quality. However, in some domains it could be difficult to make such datasets and usually it could...

  • LSUN

    The dataset used for training and validation of the proposed approach to combine semantic segmentation and dense outlier detection.

  • 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...