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https://github.com/farrajota/fast-rcnn-torch
Fast R-CNN as a package for Torch7
https://github.com/farrajota/fast-rcnn-torch
fast-rcnn package torch7
Last synced: about 12 hours ago
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Fast R-CNN as a package for Torch7
- Host: GitHub
- URL: https://github.com/farrajota/fast-rcnn-torch
- Owner: farrajota
- License: mit
- Created: 2017-05-25T14:46:14.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-08-01T13:19:17.000Z (over 7 years ago)
- Last Synced: 2023-10-20T12:34:47.661Z (about 1 year ago)
- Topics: fast-rcnn, package, torch7
- Language: Lua
- Size: 145 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Fast-RCNN package for torch7
[Fast-RCNN](https://github.com/rbgirshick/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](http://torch.ch/docs/getting-started.html)
- tds
- matio
- cudnn
- inn
- torchnet
### Package installation
To install this package you need to have [Torch7](http://torch.ch/docs/getting-started.html) installed on your machine and some other packages. To install this packages, simply do:
```bash
luarocks install tds
luarocks install cudnn
luarocks install inn
luarocks install matio
luarocks install torchnet
```
Finally, to install this package do the following:
```bash
git clone https://github.com/farrajota/fast-rcnn-torch
cd fast-rcnn-torch && luarocks make rocks/*
```
# Usage
To call this package simply do:
```lua
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
```lua
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
```lua
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
```lua
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.
```lua
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](https://github.com/farrajota/fastrcnn-example-torch) 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](https://github.com/farrajota/pedestrian_detector_torch) 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](https://github.com/rbgirshick/fast-rcnn), [Fast-RCNN for Torch7](https://github.com/mahyarnajibi/fast-rcnn-torch) and [facebook/multipathnet](https://github.com/facebookresearch/multipathnet).