help='dataset (cifar10 or cifar100[default])') parser.add_argument('--batch-size', type=int, default=100, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--num_classes', type=int, default=100) ...
CIFAR-100 Dataset 是用于机器视觉领域的图像分类数据集,拥有 20 个大类,共计 100 个小类,其中每个小类包含 600 张图像(500 张训练图像和 100 张测试图像)并且每张图像均有一个小标签和一个大标签。对于每一张图像,他有fine_labels和coarse_labels两个标签,分别代表图像的细粒度和粗粒度标签,对应下图的classes和...
CIFAR-100 Dataset is provided by Canadian Institute for Advanced Research.本数据集由加拿大高级研究所提供。 数据列表 数据名称上传日期大小下载 cifar100_test.zip2021-02-2823.34MB cifar100_valid.zip2021-02-2823.36MB cifar100_train.zip2021-02-2893.12MB ...
官方描述:The CIFAR-10 and CIFAR-100 are labeledsubsetsof the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. (CIFAR-10和CIFAR-100是一个有8千万小图片数据集的子集,由Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton收集) 下载:http://...
cifar100.zip (172.86M) 下载 File Name Size Update Time cifar100/test/aquatic_mammals/beaver/beaver_s_000019.png 3049 2018-10-09 06:06:02 cifar100/test/aquatic_mammals/beaver/beaver_s_000026.png 3120 2018-10-09 06:06:08 cifar100/test/aquatic_mammals/beaver/beaver_s_000030.png 3075 2018...
test_dataset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=transform_test) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False, num_workers=2) # 定义卷积神经网络模型 ...
CIFAR 100数据集,100种fine分类(20种coarse分类),已对数据进行处理,包含train_list.txt和val_list.txt(对应fine和coarse两种版本),适配paddleclass的使用,直接在配置文件里面修改对应的路径即可 - 飞桨AI Studio
Hardware Environment(Ascend/GPU/CPU): Uncomment only one/device <>line, hit enter to put that in a new line, and remove leading whitespaces from that line: /device ascend /device gpu /device cpu Software Environment: -- MindSpore version (source or binary): ...
lightbulb See what others are saying about this dataset What have you used this dataset for? Learning 0Research 0Application 0LLM Fine-Tuning 0 How would you describe this dataset? Well-documented 0Well-maintained 0Clean data 0Original 0High-quality notebooks 0Other text_snippet Metadataunfold_mor...
test_dataset = datasets.cifar.CIFAR100(root='cifar100', train=False, transform=None, download=True) 1. 2. 3. 4. 可以看到有四个参数: root:数据集文件的存储路径。 train:是否为训练集。True则表示视为训练集,False表示视为测试集。 transform:所应用的数据扩充方法。