Custom Implementation of Non-Deep Networks

Related tags

Deep Learning ParNet
Overview

ParNet

Custom Implementation of Non-deep Networks

arXiv:2110.07641
Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun

Official Repository https://github.com/imankgoyal/NonDeepNetworks

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.

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Comments
  • Really faster than ResNet? I am very confused

    Really faster than ResNet? I am very confused

    Hello, my friend, appreciate for your great work! I have tested your code and change the ResNet code in my model by using your ParNet , but the actual time is quite slow than the paper said. My block size is [64, 128, 256, 512, 2048], and the time of "forward()" is more than 5s average while the Resnet is 0.02s in my device. I have use the time function for every line in the forward(), find that the encode stuff is the main reason. I continue write time.perf_counter() in the encode stuff, find that the "self.stream2_fusion" and "self.stream3_fusion" is the most time user. Do you know why ?

    opened by StonepageVan 2
  • AvgPool2d in Downsample

    AvgPool2d in Downsample

    In Downsample, I think it should be torch.nn.AvgPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), not torch.nn.AvgPool2d(kernel_size=(2,2)). otherwise x & y have wrong size, and x + y will throw exception.

    opened by JJZHK 1
Owner
Pritama Kumar Nayak
Pritama Kumar Nayak
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