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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We introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. We collected and annotated images ourselves. Our dataset consists of 6135 images across a di- verse set of 147 object categories, from kitchen utensils and office stationery to vehicles and animals. The object count in our dataset varies widely, from 7 to 3731 objects, with an average count of 56 objects per image. In each image, each object instance is annotated with a dot at its approxi- mate center. In addition, three object instances are selected randomly as exemplar instances; these exemplars are also annotated with axis-aligned bounding boxes.
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HowMany-Qa is a object counting dataset. It is taken from the counting-specific union of VQA 2.0 (Goyal et al., 2017) and Visual Genome QA (Krishna et al., 2016).
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The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model.
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
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By releasing this dataset, we aim at providing a new testbed for computer vision techniques using Deep Learning. The main peculiarity is the shift from the domain of "natural images" proper of common benchmark dataset to biological imaging. We anticipate that the advantages of doing so could be two-fold: i) fostering research in biomedical-related fields - for which popular pre-trained models perform typically poorly - and ii) promoting methodological research in deep learning by addressing peculiar requirements of these images. Possible applications include but are not limited to semantic segmentation, object detection and object counting. The data consist of 283 high-resolution pictures (1600x1200 pixels) of mice brain slices acquired through a fluorescence microscope. The final goal is to individuate and count neurons highlighted in the pictures by means of a marker, so to assess the result of a biological experiment. The corresponding ground-truth labels were generated through a hy
This dataset is a collection of fluorescent images from mice in order to test an automatic cell counting tool that we developed. 62 images viewed from 2 or 3 different fields of views are shown. In brief, the dataset was derived from brain sections of a model for HIV-induced brain injury (HIVgp120tg), which expresses soluble gp120 envelope protein in astrocytes under the control of a modified GFAP promoter. The mice were in a mixed C57BL/6.129/SJL genetic background, and two genotypes of 9 month old male mice were selected: wild type controls (Resting, n = 3) and transgenic littermates (HIVgp120tg, Activated, n = 3). No randomization was performed. HIVgp120tg mice show among other hallmarks of human HIV neuropathology an increase in microglia numbers which indicates activation of the cells compared to non-transgenic littermate controls.
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To effectively evaluate OmniCount across open-vocabulary, supervised, and few-shot counting tasks, a dataset catering to a broad spectrum of visual categories and instances featuring various visual categories with multiple instances and classes per image is essential. The current datasets, primarily designed for object counting focusing on singular object categories like humans and vehicles, fall short for multi-label object counting tasks. Despite the presence of multi-class datasets like MS COCO, their utility is limited for counting due to the sparse nature of object appearance. Addressing this gap, we created a new dataset with 30,230 images spanning 191 diverse categories, including kitchen utensils, office supplies, vehicles, and animals. This dataset, featuring a wide range of object instance counts per image ranging from 1 to 160 and an average count of 10, bridges the existing void and establishes a benchmark for assessing counting models in varied scenarios.
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