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Pascal VOC data sets. Data sets from the VOC challenges are available through the challenge links below, and evalution of new methods on these data sets can be achieved through the PASCAL VOC Evaluation Server . The evaluation server will remain active even though the challenges have now finished.
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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.
Dec 6, 2022 · This dataset contains the data from the PASCAL Visual Object Classes Challenge, corresponding to the Classification and Detection competitions.
May 31, 2024 · Pascal VOC is a renowned dataset and benchmark suite that has significantly contributed to the advancement of computer vision research. It provides standardized image data sets for object class recognition and a common set of tools for accessing the data and evaluating the performance of computer vision models.
Nov 12, 2023 · The PASCAL VOC (Visual Object Classes) dataset is a well-known object detection, segmentation, and classification dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models.
PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are: Person: person Animal: bird, cat, cow, dog, horse, sheep Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor.
To download the training/validation data, see the development kit . The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image.
Introduction. The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are:
PASCAL VOC 2011 is an image segmentation dataset. It contains around 2,223 images for training, consisting of 5,034 objects. Testing consists of 1,111 images with 2,028 objects.
