Fast-RCNN package for torch7

Fast-RCNN implementation for Torch7 as a package with methods for training and testing an object detector network.

Features

• Simple API for training, testing, detecting and visualizing objects in images.
• Multi-threaded data loading/preprocessing;
• Multi-GPU support;
• Common data augmentation techniques (color jitter, scaling, etc.);
• Pascal VOC / MS COCO mAP evaluation schemes.
• Proposals data augmentation during train

Installation

Requirements

• NVIDIA GPU with compute capability 3.5+ (2GB+ ram)
• Torch7
• tds
• matio
• cudnn
• inn
• torchnet

Package installation

To install this package you need to have Torch7 installed on your machine and some other packages. To install this packages, simply do:

luarocks install tds
luarocks install cudnn
luarocks install inn
luarocks install matio
luarocks install torchnet

Finally, to install this package do the following:

git clone https://github.com/farrajota/fast-rcnn-torch
cd fast-rcnn-torch && luarocks make rocks/*

Usage

To call this package simply do:

local fastrcnn = require("fastrcnn")

This loads a table with the necessary methods for creating, training and testing a Fast R-CNN network. Also, it contains a method for detecting objects in images and for visualizing the detections with a window frame (requires qt to work).

train

fastrcnn.train(dataLoadTable, rois, model, modelParameters, opts)

Trains a model on a given dataset with some proposals.

Parameters

• dataLoadTable: table with methods for loading data. (type=table)
• rois: Region-of-Interest bounding box proposals. (type=table)
• model: a Fast R-CNN style network. (type=table)
• modelParameters: model parameters (color space, meanstd, pixel_scale and stride). (type=table)
• opts: training options. (type=table)

test

fastrcnn.test(dataLoadTable, rois, model, modelParameters, opt)

Test a model on a dataset (mAP score).

Parameters

• dataLoadTable: Table with methods for loading data. (type=table)
• rois: Region-of-Interest bounding box proposals. (type=table)
• model: A Fast R-CNN network. (type=table)
• modelParameters: The model's parameters (color space, meanstd, pixel_scale and stride). (type=table)
• opts: Testing options. (type=table)

detector

imdetector = fastrcnn.Detector(model, modelParameters, opt)

Object detector class. This provides a simple interface to image inference.

Parameters

• model: A Fast R-CNN network. (type=table)
• modelParameters: The model's parameters (color space, meanstd, pixel_scale and stride). (type=table)
• opts: Testing options. (type=table)

Object detector class.

scores, bboxes = imdetector:detect(im, proposals)

Receives an image and region proposals as input and outputs scores and bounding boxes.

Parameters

• im: Image tensor. (type=torch.Tensor)
• proposals: Region-of-Interest bounding box proposals (type=torch.Tensor)

utils

This package contains several utility methods for creating models, loading roi proposals from file or visualizing object detection with a window frame.

• model: methods for storing, loading and setup a Fast R-CNN model.

• nms: non-maximum suppression methods.

• load: load roi proposals from file (ony Matlab files atm).

• visualize_detections: visualize detections with a window frame.

Note: visualize_detections requires qt to work. For that, you need to use the qlua interpreter.

Demos

This repo contains code examples on how to train+test an object detector using this module for the Pascal VOC 2007, 2012 and MS COCO datasets.

Another repo contains code examples on how to train+test an object detector for pedestrian detection on the Caltech Pedestrian dataset.

License

MIT license (see the LICENSE file)

Acknowledgements

This package was heavily inspired by the following repositories: Fast-RCNN, Fast-RCNN for Torch7 and facebook/multipathnet.