482 datasets found

The Pascal VOC, Pascal Context, COCO-Object, Cityscapes, and ADE20k datasets are used for evaluation of the proposed method.

SA-1B is a large-scale benchmark for instance segmentation and object detection.

LVIS is a large-scale benchmark for instance segmentation and object detection.

EdgeSAM is an accelerated variant of the Segment Anything Model (SAM) optimized for efficient execution on edge devices with minimal compromise in performance.

MS COCO: Common Objects in Context

MMDetection: Open MMLab Detection Toolbox and Benchmark

The dataset used in the paper for 2-D and 3-D object detection, including aerial images, scene text, and face detection.

The dataset used in the paper for 2-D and 3-D object detection, including aerial images, scene text, and face detection.

The COCO dataset is a large-scale dataset for object detection and image classification.

Multispectral object detection for autonomous vehicles

Multispectral Object Detection with Deep Learning

The COCO 2014 validation set is used for evaluating the performance of the proposed method.

Linley et al. (2014) - Microsoft COCO

The COCO-stuff dataset is a large-scale dataset for scene understanding, object detection, and image synthesis.

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

A benchmark for object detection

Object detection using a top-down visual attention model

DIW dataset is a dataset containing images labelled with relative depths. It is used for depth estimation and object detection tasks.

The dataset is used for object detection and superpixel segmentation tasks.

The CAMO dataset is the first COD dataset, containing 1,250 camouflaged images and 1,250 non-camouflaged images.