15 datasets found

The OpenImages dataset V6 contains images with annotations for object detection and instance segmentation tasks.

The dataset used in the paper is the VOC dataset, which is a dataset for object detection and segmentation.

Inria Aerial Image Labeling dataset contains aerial orthorectified color imagery of 5000 × 5000 pixels with a spatial resolution of 0.3 m.

AICrowd Mapping Challenge dataset contains 300 × 300 pixels RGB images and corresponding annotations in MS-COCO format.

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

The dataset used in the paper is Broden, a dataset containing pixel-level concept annotations.

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

The MSCOCO dataset is a large-scale image captioning dataset, containing 113,287 images with 5,000 validation images and 5,000 test images. The dataset is used for training and...

The Objects365 dataset is a large-scale object detection dataset containing 365,000 images with 365 categories.

MIR Flickr dataset

The LabelMe dataset is a natural scene dataset used for testing the performance of the IBTM model on image classification tasks.

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...