249 datasets found

The CrowdHuman dataset is a benchmark dataset for human detection, consisting of 15,000 images, 4,370 images for validation, and 5,000 images for testing.

Object detection is a fundamental task in computer vision, requiring large annotated datasets that are difficult to collect.

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 PSU dataset was collected from two sources: an open dataset of aerial images available on Github and our own images acquired after flying a 3DR SOLO drone equipped with a...

The Stanford dataset consists of a large-scale collection of aerial images and videos of a university campus containing various agents (cars, buses, bicycles, golf carts,...

The MS COCO captions dataset contains captions for images in the Microsoft COCO dataset.

The PASCAL VOC 2010 dataset is an extension of the PASCAL VOC dataset, containing additional images and categories.

The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as...

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...

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,...

Large-scale vision-and-language models trained on curated and web-scrapped data have led to significant improvements over task-specific models when transferred to downstream...

The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

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...

Underwater object detection dataset

Underwater object detection dataset

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...