ImageNet-C is an open source data set that consists of algorithmically generated corruptions (blur, noise) applied to the ImageNet test-set.
Tiny ImageNet-C is an open-source data set comprising algorithmically generated corruptions applied to the Tiny ImageNet (ImageNet-200) test set comprising 200 classes following the concept of ImageNet-C. It was introduced by Hendrycks et al. ("Benchmarking Neural Network Robustness to Common ...
这个比赛使用了Places2 dataset,比赛规则是对于给定图像,允许算法产生5个场景分类,并挑选匹配度最高的作为评估结果,详看他们的评估规则吧。为什么这么做呢?因为同一幅图片可以包含有多个场景类别,事实上同一幅图片本来就是用多个类别标注的。 场景分析 这个比赛的目标是将图像分割成与语义类别相关联的不同图像区域,如天...
ImageNet国际计算机视觉挑战赛(ILSVRC) —— ImageNet Large Scale Visual Recognition Competition 2 COCO common objects Dataset COCO数据集由微软赞助,其对于图像的标注信息不仅有类别、位置信息,还有对图像的语义文本描述,COCO数据集的开源使得近两三年来图像分割语义理解取得了巨大的进展,也几乎成为了图像语义理解算法...
Dataset pruning for ImageNet and LAION-2B. Contribute to BAAI-DCAI/Dataset-Pruning development by creating an account on GitHub.
train_dataset = datasets.ImageFolder(root=train_dir, transform=transform) val_dataset = datasets.ImageFolder(root=val_dir, transform=transform) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) 总结 以上步骤...
63 'raw_data_dir', None, 'Directory path for raw Imagenet dataset. ' 64 'Should have train and validation subdirectories inside it.') Merge internal changes into public repository (change 214399938) 4 years ago 65 flags.DEFINE_boolean( 66 'gcs_upload', True, 'Set ...
set of 60 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset... A Wong 被引量: 6发表: 2018年 高分辨卫星图像卷积神经网络分类模型 直接训练模型直接在文章提出的数据集上进行训练,预训练模型先在ILSVRC(the ...
Dataset 作者使用了电力传输与配电基础设施图像(ETDII)数据集进行实验,这是一个来自杜克大学的公开数据集。该数据集的来源包括CT ECO、USGS、LINZ和SpaceNet等不同提供者。它由494个图像块组成,来自六个国家,分别是美国、苏丹、新西兰、墨西...
(PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66%). To our knowledge, our result is the first to surpass human-level performance (5.1%, Russakovsky et al.) on ...