Custom Implementation of Non-deep Networks

Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun

Official Repository

Overview: Depth is the hallmark of DNNs. But more depth means more sequential computation and higher latency. This begs the question — is it possible to build high-performing “non-deep” neural networks? We show that it is. We show, for the first time, that a network with a depth of just 12 can achieve top-1 accuracy over 80% on ImageNet, 96% on CIFAR10, and 81% on CIFAR100. We also show that a network with a low-depth (12) backbone can achieve an AP of 48% on MS-COCO.

If there is any issue in the code, please feel free to update.


View Github