MSDNet - reproducibility and applying GCN blocks with separable kernel
This repository contains a reproduction code (in PyTorch) for "MSDNet: Multi-Scale Dense Networks for Resource Efficient Image Classification"
Contents
Introduction
MSDNet is a novel approach fo image classification with computational resource limits at test time. This repository provides an implementation based on the technical description provided in the paper. Currently this code implements the support for Cifar-10 and Cifar-100.
Moreover, this code integrates the support for GCN based layers instead of normal convolution layers, in order to reduce the model parameters.
Usage
Dependencies
Train
As an example, use the following command to train an MSDNet on Cifar10
python3 main.py --model msdnet -b 64 -j 2 cifar10 --msd-blocks 10 --msd-base 4 \
--msd-step 2 --msd-stepmode even --growth 6-12-24 --gpu 0
As an example, use the following command to train an MSDNet on Cifar100 with GCN block
python3 main.py --model msdnet -b 64 -j 2 cifar100 --msd-blocks 10 --msd-base 3 \
--msd-step 1 --msd-stepmode even --growth 6-12-24 --gpu 0 --msd-gcn --msd-gcn-kernel 5 \
--msd-share-weights --msd-all-gcn
Evaluation
We take the Cifar10 model trained above as an example.
To evaluate the trained model, use evaluate to evaluate from the default checkpoint directory:
python3 main.py --model msdnet -b 64 -j 2 cifar100 --msd-blocks 10 --msd-base 3 \
--msd-step 1 --msd-stepmode even --growth 6-12-24 --gpu 0 --msd-gcn --msd-gcn-kernel 5 \
--msd-share-weights --msd-all-gcn --resume --evaluate
Other Options
For detailed options, please python main.py --help
For more examples and using pre-trained models, please less script.sh