The codes are PyTorch re-implement version for paper: SqueezeNext: Hardware-Aware Neural Network Design. (SqueezeNext)

Gholami A, Kwon K, Wu B, et al. SqueezeNext: Hardware-Aware Neural Network Design[J]. 2018. arXiv:1803.10615v1

We implement this work from amirgholami/SqueezeNext.

Here, we use a variation of the latter approach by using a two stage squeeze layer. In each SqueezeNext block, we use two bottleneck modules each reducing the channel size by a factor of 2, which is followed by two separable convolutions. We also incorporate a final 1 × 1 expansion module, which further reduces the number of output channels for the separable convolutions.

• jupyter notebook
• Python3
• PyTorch 0.4

We just test four models in three datasets: Cifar10, Cifar100 and Tiny ImageNet