Implementation of some object detection frameworks in torch.
You should probably check the refactoring branch of this repository, which simplifies the code structure, and also contains an implementation of Fast-RCNN and threaded RCNN (making it much faster than standard RCNN). It will be merged to master soon.
It requires the following packages
To install them all, do
## xml
luarocks install xml
## matio
# OSX
brew install libmatio
# Ubuntu
sudo apt-get install libmatio2
luarocks install matio
To install hdf5, follow the instructions in here
First, clone this repo
git clone https://github.com/fmassa/object-detection.torch.git
The zeiler pretrained model is available at https://drive.google.com/open?id=0B-TTdm1WNtybdzdMUHhLc05PSE0&authuser=0.
It is supposed to be at data/models.
If you want to use your own model in SPP framework, make sure that it follows the pattern
model = nn.Sequential()
model:add(features)
model:add(pooling_layer)
model:add(classifier)
where features can be a nn.Sequential of several convolutions and pooling_layer is the last pooling with reshaping of the data to feed it to the classifer. See models/zeiler.lua for an example.
To finetune the network for detection, simply run
th main.lua
To get an overview of the different parameters, do
th main.lua -h
The default is to consider that the dataset is present in datasets/VOCdevkit/VOC2007/.
The default location of bounding boxes .mat files (in RCNN format) is supposed to be in data/selective_search_data/.