研究者在 ImageNet-1K 和 ImageNet-100 数据集上训练和验证 DRNet 模型，其中 ImageNet-100 是 ImageNet-1K 的子集。ImageNet-100 实验 从下表 1 可以看出，在 ImageNet-100 数据集上，DRNet 相比于 ResNet-50，减少了 17% 的 FLOPs，同时获得了 4.0% 的准确率提升。当调整超参数和时，可以减少 32%...
imagenet-100/train/n01440764/n01440764_10040.JPEG 146489 2021-08-02 13:53:18 imagenet-100/train/n01440764/n01440764_10042.JPEG 6350 2021-08-02 13:53:18 imagenet-100/train/n01440764/n01440764_10043.JPEG 68487 2021-08-02 13:53:18 imagenet-100/train/n01440764/n01440764_10048.JPEG...
在三个(Caltech-256、QuickDraw和Amazon Reviews)含大量错误标签的数据集中,研究人员随机检查了部分样本(分别是8.6%、0.04%、0.02%),对其它数据集则对所有识别到的错误标签进行检查,如下表所示。(注意,由于ImageNet测试集不公开,所以这里使...
历经五个多月的厮杀,中国科学院自动化研究所及中国科学院自动化研究所南京人工智能芯片创新研究院联合团队获得ImageNet和CIFAR-100双项冠军。 结合极低比特量化技术和稀疏化技术,他们团队在ImageNet任务上相比主办方提供的基准模型取得了20.2倍的压缩率和12.5倍的加速比,在CIFAR-100任务上取得了732.6倍的压缩率和356.5...
Explore and run machine learning code with Kaggle Notebooks | Using data from ImageNet100
Security Insights ImageNet100#25 New issue Closed Description ChongjianGE ceezy767 commentedon Dec 19, 2019 ceezy767 ChongjianGE commentedon Dec 19, 2019 ChongjianGE ChongjianGE closed this ascompletedon Dec 19, 2019 Sign up for freeto join this conversation on GitHub.Already have an account?
Hi @HobbitLong, thanks for your great work and also sharing the code. I guess the ImageNet-100 is not a conventional subset so I wonder if you can share the list since we also don't have enough resources to run on the full ImageNet ==.
tiny-imagenet-100-A.ziptiny-imagenet-100-A.zip 222.0 MB 压缩文件内容 文件名文件大小 tiny-imagenet-100-A/test/images/test_0.JPEG 1.1 KB tiny-imagenet-100-A/test/images/test_1.JPEG 2.2 KB tiny-imagenet-100-A/test/images/test_10.JPEG 1.8 KB tiny-imagenet-100-A/test/images/test_10...
如果模型到10亿,训练ImageNet会过拟合,论文采用了自监督学习方法MAE中的预训练-微调范式对大规模ViTAE模型的训练方式进行优化,并基于此对ViTAE模型的效果进行了分析。 MAE在预训练阶段采用随机采样的图片块作为输入。这样高度稀疏的离散图片块缺失了空间连续信息,难以让NC中的卷积分支学到合适的空间特征表示。
Our method achieves the state-of-the-art performance on ImageNet, 80.7% top-1 accuracy with 194M FLOPs. Combined with PWLU activation function and CondConv, CoE further achieves the accuracy of 80.0% with only 100M FLOPs for the first time. More importantly, our method is hardware friendly...