482 datasets found

The dataset used for object detection on streaming video from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2015.

The dataset used in the paper is ImageNet-1K, ADE20K, and COCO 2017.

COCO Stuff dataset is an extension of the COCO dataset, 164,000 images covering 171 classes are annotated with segmentation masks.

The VOC07 dataset is used for evaluating the performance of object detection and image classification methods.

The dataset used for object detection in industrial scenarios, with a focus on small objects.

The Caltech dataset is a dataset of images used to evaluate the performance of the ODIN system.

The Microsoft Common Objects in Context (COCO) dataset is a benchmark for object detection, segmentation, and image classification.

The Pascal Visual Object Classes (VOC) dataset is a benchmark for object detection and image classification.

The Habitat dataset is a large-scale indoor simulator dataset containing 145 semantically-annotated indoor scenes.

The dataset used in this paper for image classification and object detection tasks.

The CUB dataset is a collection of images of landbirds and waterbirds from the CUB dataset, combined with images from Places dataset as background.

Detectron is a library for object detection.

Object detectors are usually equipped with backbone networks designed for image classification. It might be sub-optimal because of the gap between the tasks of image...

The Hazy-COCO dataset is a synthetic dataset generated by adding haze to the MS COCO dataset.

The proposed YOLO-Former method seamlessly integrates the ideas of transformer and YOLOv4 to create a highly accurate and efficient object detection system.

The Stanford Drone Dataset is a well-established benchmark for human trajectory prediction in bird’s eye view. The dataset consists of 20 scenes captured using a drone in...

The authors used the RefCOCO dataset, a large-scale dataset for object detection and scene understanding, to train and evaluate their models.

DiffusionDet is a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes.

SSD is a single-shot object detection algorithm.

Objects for detection usually have distinct characteristics in different sub-regions and different aspect ratios. However, in prevalent two-stage object detection methods,...