MSRSegNet

Efficient Multi-Scale Residual Network for Image Classification and Semantic Segmentation

Results

• Our segmentation network achieves an mIOU of 81 on the PASCAL VOC dataset while running at about 21 fps on NVIDIA TitanX Pascal.

http://host.robots.ox.ac.uk:8080/anonymous/NQRTFB.html

• Our segmentation network achieves an mIOU of 61.12 on the PASCAL VOC dataset while running at about 60 fps on NVIDIA TitanX Pascal with an input image of size 224 x 224.

http://host.robots.ox.ac.uk:8080/anonymous/IJV89W.html

• Our classification network achieves a top-5 error of 8.47% on the ImageNet Validation dataset.

Datasets

• Download the CamVid dataset from below github repository

https://github.com/alexgkendall/SegNet-Tutorial

• Download the PASCAL VOC 2012 dataset using below link

wget http://cvlab.postech.ac.kr/research/deconvnet/data/VOC2012_SEG_AUG.tar.gz

Training Semantic Segmentation Models

You can train the network as:

• Training M-RiR using GPU-0 on the CamVid dataset

CUDA_VISIBLE_DEVICES=0 th main.lua --dataset cv --imHeight 384 -imWidth 480 --modelType 2 -lr 0.0001 -d 10 -de 300 -optimizer adam -maxEpoch 100

• Training M-Plain using GPU-0 and GPU-1 on the CAMVID dataset

CUDA_VISIBLE_DEVICES=0,1 th main.lua --dataset cv --imHeight 384 -imWidth 480 --modelType 1 -lr 0.0001 -d 10 -de 300 -optimizer adam -maxEpoch 100

• Training M-Hyper on the CAMVID dataset

    CUDA_VISIBLE_DEVICES=1,2 using GPU-1 and GPU-2 th main.lua --dataset cv --imHeight 384 -imWidth 480 --modelType 3 -lr 0.0001 -d 10 -de 300 -optimizer adam -maxEpoch 100
  

Training Image Classification Models

• For training on the ImageNet and the Cifar datasets, please use the scripts provided by FaceBook AI Research
• Once you get the source code from FaceBook AI Research github repository, please replace the content of resnet.lua by mresnet_class.lua
• Follow the instructions mentioned on FaceBook AI Research Github page for training.
• For doing depth related studies, please follow below command

To train MResNet on Cifar10 dataset with a depth of 11

th main.lua -dataset cifar10 -nGPU 2 -batchSize 128 -depth 1

To train MResNet on Cifar100 dataset with a depth of 20

th main.lua -dataset cifar100 -nGPU 2 -batchSize 128 -depth 2

License

This software is released under a creative commons license which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here.