Caltech-101 Dataset 是由 101 个类别的对象图片组成的数据集,它主要用于目标识别和图像分类。包含来自 101 个物体类别的约 9000 张图像,不同类别有 40 至 800 张图片,每张图片的大小在 300 * 200 像素,且数据集的发布者均已标注对应的目标以供使用。选择这些类别是为了反映现实世界中的各种物体,而图像本身也...
创建自定义数据集和Loaders 1#custom dataset2classImageDataset(Dataset):3def__init__(self, images, labels=None, transforms=None):4self.X =images5self.y =labels6self.transforms =transforms78def__len__(self):9return(len(self.X))1011def__getitem__(self, i):12data =self.X[i][:]1314ifs...
The Caltech 101 dataset consists of a total of 9146 images, split between 101 different object categories, as well as an additional background/clutter category.Each object category contains between 40 and 800 images on average. Common and popular categories such as faces tend to have a larger ...
show_annotations.m. The Caltech 101 dataset consists of a total of 9146 images, split between 101 different object categories, as well as an additional background/clutter category. Each object category contains between 40 and 800 images on average. Common and popular categories such as faces ten...
(self): return 6544 #train_set = TRAIN() #train_loader = DataLoader(train_set,shuffle=False,batch_size=1) train_dataset = TRAIN(transform=train_transforms) train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True) model=paddle.vision.models.resnet101(pretrained=True,...
Caltech-101 Dataset 是由 101 个类别的对象图片组成的数据集,它主要用于目标识别和图像分类。不同类别有 40 至 800 张图片,每张图片的大小在 300 * 200 像素,且数据集的发布者均已标注对应的目标以供使用。 该数据集由加州理工学院的李菲菲、马克安德烈托和 Marc’Aurelio Ranzato 于 2003 年 9 月收集,相关...
caltechdatasetvisionarchivecomputational加州 CaltechComputationalVisionGroupArchive-Caltech101dataset(加州理工大学计算视觉组档案-加州理工大学101数据集)数据摘要:Picturesofobjectsbelongingto101categories.About40to800imagespercategory.Mostcategorieshaveabout50images.CollectedinSeptember2003byFei-FeiLi,MarcoAndreetto,andMa...
def train_mapper(sample): img, label = sample #img = Image.open(img) img = paddle.dataset.image.load_image(img) # 统一图片大小 img = paddle.dataset.image.simple_transform(im=img, #输入图片是HWC resize_size=200, # 剪裁图片 crop_size=200, is_color=True, #彩色图像 is_train=False) ...
The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether ...
Caltech-101 Dataset 是由 101 个类别的对象图片组成的数据集,它主要用于目标识别和图像分类。不同类别有 40 至 800 张图片,每张图片的大小在 300 * 200 像素,且数据集的发布者均已标注对应的目标以供使用。 该数据集由加州理工学院的李菲菲、马克安德烈托和 Marc’Aurelio Ranzato 于 2003 年 9 月收集,相关...