This repository contains the Caffe implementation of SqueezeNext. Each directory contains the network definition along with the solver parameters used for training. For visualizing each architecture you can use netscope ( you need to copy the train_val.prototxt files). For details of each architecture please see this paper.
The corresponding trained caffemodel files are also available here. All networks were trained using IntelCaffe on 32 Intel KNightsLanding (KNL) using the same hyper-parameters. Fine-tuning the hyper-parameters for each model may lead to better results. Our goal has been to show the general trend but please contact us if you got new results.
- For TensorFlow implementation, please see Timen's implementation
- For Pytorch implementation, please see osmr's implementation
- For results on these datasets, please see luuuyi's repository
1.0/2.0：其差别为除去第一个大卷积核个数外，其余的所有的卷积核个数2要比1多一倍。 23/23v5：其差别为23中第一个大卷积核尺寸为77，而23v5中第一个大卷积核尺寸为55。 G/no_G：G表示当前网络的block中，第一个11卷积，31卷积和1*3卷积都用到了group=2的操作。