13 datasets found

Microsoft COCO 2014 and 2017 datasets for object detection, segmentation, and captioning

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

The MS COCO dataset is a large benchmark for image captioning, containing 328K images with 5 caption descriptions each.

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 dataset used in this paper is a multimodal large language model (LLaMM) dataset, specifically POPE, which consists of 7 billion parameters and is used for multimodal tasks...

The dataset used in the paper is YFCC100M, a large-scale video dataset. The dataset is used for foreground and background patch extraction and object recognition tasks.

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

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 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.

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 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...

Human Pose Estimation (HPE) aims to estimate the position of each joint point of the human body in a given image. HPE tasks support a wide range of downstream tasks such as...