21 datasets found

A large-scale dataset for object detection and instance segmentation.

A dataset for amodal instance segmentation, consisting of images with variable numbers of object instances.

iSAID dataset is a large-scale high-resolution remote sensing dataset for aerial instance segmentation, containing many small objects with complex backgrounds.

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.

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

The COCO 2017 Dataset is a large-scale benchmark dataset for object detection, semantic segmentation, and instance segmentation.

LVIS is a large-vocabulary object detection and instance segmentation dataset.

The COCO test-dev dataset is used for instance segmentation. It contains 20k test-dev images.

A large dataset of images of underwater trash collected from a variety of sources, annotated with bounding boxes and segmentation labels.

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...

Object detection is one of the most foundational computer vision task and is essential for many real-world applications. The object detection pipeline has been developed...

The COCO dataset is a large-scale dataset for object detection, semantic segmentation, and captioning. It contains 80 object categories and 1,000 image instances per category,...

The COCO2017 dataset is used for the trainability experiment, which includes 47,429 images with 210,893 objects.

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...

The dataset used in the paper for instance segmentation, which includes COCO, PASCAL VOC, Cityscapes, and LVIS datasets.

Instance segmentation (IS) is an important computer vision task, aiming at simultaneously predicting the class label and the binary mask for each instance of interest in an image.

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...

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...

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...