cifar10=torchvision.datasets.CIFAR10(root='datasets',train=True,download=False)cifar10_test=torchvision.datasets.CIFAR10(root='datasets',train=False,download=False)print(cifar10)print(cifar10_test) 可以得到返回的结果 Dataset CIFAR10 Number of datapoints: 50000 Rootlocation: datasets Split: Train Data...
接下来,使用以下Python代码下载CIFAR-10数据集: importtorchvisionimporttorchvision.transformsastransforms# 下载CIFAR-10训练集train_dataset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transforms.ToTensor())# 下载CIFAR-10测试集test_dataset=torchvision.datasets.CIFAR10(root='./...
1、下载CIFAR-10 数据集的全部数据 CIFAR-10使用方法 1、使用TF读取CIFAR-10 数据 CIFAR-10简介 官网链接:The CIFAR-10 dataset CIFAR-10是一个更接近普适物体的彩色图像数据集。CIFAR-10 是由Hinton 的学生Alex Krizhevsky 和Ilya Sutskever 整理的一个用于识别普适物体的小型数据集。一共包含10 个类别的RGB ...
(cifar_train,batch_size=batchse,shuffle=True)cifar_test=datasets.CIFAR10('cifar',False,transform=transforms.Compose([transforms.Resize((32,32)),transforms.ToTensor]),download=True)cifar_teat=DataLoader(cifar_train,batch_size=batchse,shuffle=True)x,label=iter(cifar_train).next()print('x:',x....
,# 10. 透视变换transforms.Compose([transforms.RandomPerspective(distortion_scale=0.5)]),]# 将数据增强应用于数据集并可视化增强后的图像fori,transforminenumerate(data_transforms):augmented_dataset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)# 获取一些示例图像...
OK,那我再去找_get_images_labels,也是cifar10_input.py中(奇怪为什么同一个功能要用两个函数): def_get_images_labels(batch_size,split,distords=False):"""Returns Dataset for given split."""dataset=tfds.load(name='cifar10',split=split)scope='data_augmentation'ifdistordselse'input'withtf.name_...
使用dataset下载CIFAR10数据集,并划分好训练集与测试集 使用dataloader加载数据,并设置好基本的batch_size train_ds = torchvision.datasets.CIFAR10('./data', train=True, transform=torchvision.transforms.ToTensor(),#将数据类型转化为Tensordownload=True) ...
EPOCH_CNT=200LEARN_RATE=1e-2if__name__ =='__main__':#CIFAR-10 is a subset of the Tiny Images dataset with 60000 32x32 color images of 10 classes#均值:[0.49139968 0.48215841 0.44653091] 标准差:[0.24703223 0.24348513 0.26158784]transform_train=transforms.Compose([ ...
CIFAR10('./data/CIFAR10/', train = True, download = True, transform = transform_train) train_loader = DataLoader(train_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers) val_dataset = datasets.CIFAR10('./data/CIFAR10', train = True, transform = transform_...
(10): /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/vision/datasets/cifar.py in __init__(self, data_file, mode, transform, download, backend) 127 128 # read dataset into memory --> 129 self._load_data() 130 131 self.dtype = paddle.get_default_dtype()...