Sellopale/coco-human-inpainted-objects

该数据集收集自https://www.rapidata.ai平台，包含数万个人工标注的70多种不同类别的对象。数据集包含约2000张来自COCO数据集的图像上的自由手绘标注。用户被展示一张图像并被要求用画笔工具绘制特定类别的对象 - 每张图像上只有一个这样的对象，因此任务明确。用户交互的结果是该特定图像上用户绘制的一系列线条集合。数据集的挑战在于聚合每张图像上的线条以确定目标对象的位置。对于每张图像，提供了由不同人类绘制的数百条2D线条，可用于创建目标对象的边界框和分割图。除了线条外，数据集还包含COCO 2D边界框的真实标注以及需要超越的基线预测。

The dataset was collected on the https://www.rapidata.ai platform and contains tens of thousands of human annotations of 70+ different kinds of objects. Rapidata makes it easy to collect manual labels in several data modalities with this repository containing freehand drawings on ~2000 images from the COCO dataset. Users are shown an image and are asked to paint a class of objects with a brush tool - there is always a single such object on the image, so the task is not ambiguous. The result of this user-interaction is a collection of lines drawn by the user on that particular image. The challenge of the dataset is to aggregate the lines on each image to get an idea of where the target object is. For each image, we provide hundreds of 2D lines drawn by different humans that can be used to create bounding boxes and segmentation maps on each image of the target object. Apart from the lines, the dataset contains the COCO 2D bounding box ground truths as well as baseline predictions to beat.