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% 的 FLOPs 并提升 1.8% 准确率。另外，采用分辨率感知的 BN 获得了性能提升而 FLOPs 相似。表 1 ：ResNet-50 骨干...
JFT-300M+ImageNet+10^3显卡+NAS EfficientNet大模型,常年刷新榜单。Facebook,微软等巨头也不落后,没有扩展数据集咱就用大模型,ImageNet-22K无监督学习+Transformer咱也能搞出来花样。这不,京东和悉尼大学合作,研究了ViTAEv2,6亿参数模型,ImageNet Real 91.2%最高准确率,更大模型、更多任务、更高效率。 1 ViTAE...
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 45206 2021-08-02 13:53:18 imagenet-100/train/n01440764/n01440764_10066.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...
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 ==.
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?
团队选择轻量级、同时精度略高于比赛要求的网络。最终在ImageNet上选择了MixNet-S模型(精度75.98%),在CIFAR-100上选择了DenseNet-100(精度81.1%)。 在确定好模型之后,先对网络进行剪枝,去掉不重要的参数量和计算。在这之前,对每一层进行了鲁棒性分析。具体而言,对于每一层,团队进行稀疏度从0.1到0.9的剪枝,然后...
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...
File metadata and controls Code Blame 1 lines (1 loc) · 118 Bytes Raw python -m model.bacon --est_freq 10 --ce_warmup 1 --alpha 0 --beta 0.5 --dataset_name imagenet100 --labeled_classes 50 1 While the code is focused, press Alt+F1 for a menu of operations....
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...