PASCAL-S is a dataset for salient object detection consisting of a set of 850 images from PASCAL VOC 2010 validation set with multiple salient objects on the scenes.
Our PASCAL-S dataset is built on the validation set of the PASCAL VOC 2010 segmentation challenge. This subset contains 850 natural images. In the fixation experiment, 8 subjects were instructed to perform the ¡°freeviewing¡± task to explore the images. Each image was presented for 2 ...
几乎在每一个应用领域都需要用到这三项功能,所以能否顺利的完成这三个功能,对检验一个算法的正确性和效率来说是至关重要的。所以每一个算法的设计者都会运用自己搜集到的场景图片对算法进行训练和检测,这个过程就逐渐的形成了数据集(dataset)。 而不幸的是,这样形成的数据集存在着很大的偏向性。因为就算是作者可以...
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dataset_root (str): The dataset directory. Default: None mode (str): Which part of dataset to use. it is one of ('train', 'trainval', 'context', 'val'). If you want to set mode to 'context', please make sure the dataset have been augmented. Default: 'train'. edge (bool, ...
# Download VOC 2012 dataset voc_data = datasets.VOCSegmentation(root='train_data', year='2012', image_set='train', download=True) 下载完成后获得的文件夹内容如下: SegmentationClass: 语义分割标签,只是对每个像素进行类别标注,通常是一个类别的标识符。它不关心同类中不同实例的区分。(只是区分猫和狗...
_selective_search_IJCV_top_k(split, year, top_k))defget_imdb(name):"""Get an imdb (image database) by name."""ifnot__sets.has_key(name):raiseKeyError('Unknown dataset: {}'.format(name))return__sets[name]() 开发者ID:JudeLee19,项目名称:py-faster-rcnn,代码行数:30,代码来源:fact...
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 ...
ImageSets存放的是每一种类型的challenge对应的图像数据。在ImageSets下有四个文件夹: Layout下存放的是具有人体部位的数据(人的head、hand、feet等等,这也是VOC challenge的一部分) Main下存放的是图像物体识别的数据,总共分为20类。 Segmentation下存放的是可用于分割的数据。
=None]images[image_name]=imageannotations[image_name]=detectionsANNOTATIONS_DIRECTORY=os.path.join(HOME,'annotations')MIN_IMAGE_AREA_PERCENTAGE=0.002MAX_IMAGE_AREA_PERCENTAGE=0.80APPROXIMATION_PERCENTAGE=0.75sv.Dataset(classes=CLASSES,images=images,annotations=annotations).as_pascal_voc(annotations_directory_...