The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
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The PASCAL Visual Object Classes (VOC) 2012 dataset contains 20 object categories including vehicles, household, animals, and other: aeroplane, bicycle, boat, bus, car, motorbike, train, bottle, chair, dining table, potted plant, sofa, TV/monitor, bird, cat, cow, dog, horse, sheep, and person. Each image in this dataset has pixel-level segmentation annotations, bounding box annotations, and object class annotations. This dataset has been widely used as a benchmark for object detection, semantic segmentation, and classification tasks. The PASCAL VOC dataset is split into three subsets: 1,464 images for training, 1,449 images for validation and a private testing set.
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PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are:
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The Car Parking Lot Dataset (CARPK) contains nearly 90,000 cars from 4 different parking lots collected by means of drone (PHANTOM 3 PROFESSIONAL). The images are collected with the drone-view at approximate 40 meters height. The image set is annotated by bounding box per car. All labeled bounding boxes have been well recorded with the top-left points and the bottom-right points. It is supporting object counting, object localizing, and further investigations with the annotation format in bounding boxes.
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The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. It is useful for training a device such as a deep neural network to learn to detect and/or count cars.
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Few-Shot Object Detection Dataset (FSOD) is a high-diverse dataset specifically designed for few-shot object detection and intrinsically designed to evaluate thegenerality of a model on novel categories.
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SmartCity consists of 50 images in total collected from ten city scenes including office entrance, sidewalk, atrium, shopping mall etc.. Unlike the existing crowd counting datasets with images of hundreds/thousands of pedestrians and nearly all the images being taken outdoors, SmartCity has few pedestrians in images and consists of both outdoor and indoor scenes: the average number of pedestrians is only 7.4 with minimum being 1 and maximum being 14.
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RSOC is a large-scale object counting dataset with remote sensing images, which contains four important geographic objects: buildings, crowded ships in harbors, large-vehicles and small-vehicles in parking lots.
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iWildCam 2021 is a dataset for counting the number of animals of each species that appear in sequences of images captured with camera traps. The training data and test data are from different cameras spread across the globe. The set of species seen in each camera overlap but are not identical. The challenge is to categorize species and count the number of individuals across image bursts.
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