Awesome

New

A simplified version of MELM with context in PyTorch is released [here] by vasgaowei.

Prerequisites

Linux (tested on ubuntu 14.04LTS)
NVIDIA GPU + CUDA CuDNN
Torch7

Getting started

Install the dependencies

luarocks install hdf5 matio protobuf rapidjson loadcaffe xml

Download dataset, proposals and ImageNet pre-trained model

Download VOC2007 from: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

Download proposals from: https://dl.dropboxusercontent.com/s/orrt7o6bp6ae0tc/selective_search_data.tgz

Download VGGF from: http://www.robots.ox.ac.uk/~vgg/software/deep_eval/releases/bvlc/VGG_CNN_F.caffemodel https://gist.githubusercontent.com/ksimonyan/a32c9063ec8e1118221a/raw/6a3b8af023bae65669a4ceccd7331a5e7767aa4e/VGG_CNN_F_deploy.prototxt

The data folder has the following structure:
```
$MELM/data/datasets/VOCdevkit_2007/
$MELM/data/datasets/VOCdevkit_2007/VOCcode
$MELM/data/datasets/VOCdevkit_2007/VOC2007
$MELM/data/datasets/VOCdevkit_2007/...
$MELM/data/datasets/proposals/
$MELM/data/models/
$MELM/data/results/
```

Install functions

cd ./MELM
export DIR=$(pwd)   

cd $DIR/utils/c-cuda-functions
sh install.sh

cd $DIR/layers
luarocks make

Train and test

cd $DIR
sh Run_MELM.sh 0 VOC2007 VGGF SSW 0.1 None melm

Acknowledgements

This work would not have been possible without prior work: Vadim Kantorov's contextlocnet, Spyros Gidaris's LocNet, Sergey Zagoruyko's loadcaffe, Facebook FAIR's fbnn/Optim.lua.