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In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture".
#6 best model for Image Classification on ImageNet
Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories.
SOTA for Image Classification on iNaturalist
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
#22 best model for Image Classification on ImageNet
We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).
#15 best model for Image Classification on ImageNet
Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network.
#10 best model for Image Classification on ImageNet
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
#10 best model for Object Detection on PASCAL VOC 2007
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change.
#20 best model for Image Classification on ImageNet