This repo is the pytorch implementation of the paper from Google: MixConv: Mixed Depthwise Convolutional Kernels
This code mimics the implementation from the offical repo in Tensorflow (https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet)
Python 3.5+
PyTorch v1.0.0+
python train_cifar.py --lr 0.016 --batch-size 256 -a mixnet-s --dtype cifar100 --optim adam --scheduler exp --epochs 650
| Network | Top 1 | #Params | #Flops |
|---|---|---|---|
| Mixnet-S | in progress | 2.7M (this code) | 3.2M (this code) |
| Mixnet-M | in progress | 3.6M (this code) | 4.4M (this code) |
| Mixnet-L | in progress | 5.8M (this code) | Bug issue (solved soon) |
| Network | Top 1 | #Params | #Flops |
|---|---|---|---|
| Mixnet-S | in progress | 4.1M (this code) | 259M (this code) |
| Mixnet-M | in progress | 5.0M (this code) | 360M (this code) |
| Mixnet-L | in progress | 7.3M (this code) | 580M (this code) |
Currently, the accuracy is very low compare with the numbers reported in the paper. So, welcome scientific, rigorous ,and helpful feedbacks to train MixConv proper in Pytorch.