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Disclaimer
This hierarchy of feature detection is the core of CNN function. CNNs with Matlab. The example figures above were generated with Matlab. Convolutional Neural Networks were introduced in the Neural Network Toolbox in Matlab R2016a (e.g. Webinare on CNNs with Matlab). Here is our corresponding Matlab code for. Matlab source codes section Download MATLAB source code freerce-codes 1. MATLAB Source Codes This section on rf-wireless world website covers basic matlab codes which can be easily integrated by enthusiasts and engineers working in MATLAB. The codes are mainly targeted for signal processing applications.
The official Faster R-CNN code (written in MATLAB) is available here.If your goal is to reproduce the results in our NIPS 2015 paper, please use the official code.
This repository contains a Python reimplementation of the MATLAB code.This Python implementation is built on a fork of Fast R-CNN.There are slight differences between the two implementations.In particular, this Python port
- is ~10% slower at test-time, because some operations execute on the CPU in Python layers (e.g., 220ms / image vs. 200ms / image for VGG16)
- gives similar, but not exactly the same, mAP as the MATLAB version
- is not compatible with models trained using the MATLAB code due to the minor implementation differences
- includes approximate joint training that is 1.5x faster than alternating optimization (for VGG16) -- see these slides for more information
By Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun (Microsoft Research)
This Python implementation contains contributions from Sean Bell (Cornell) written during an MSR internship.
Please see the official README.md for more details.
Faster R-CNN was initially described in an arXiv tech report and was subsequently published in NIPS 2015.
License
Faster R-CNN is released under the MIT License (refer to the LICENSE file for details).
![Convolution matlab example Convolution matlab example](/uploads/1/2/6/2/126275858/868921769.jpg)
Citing Faster R-CNN
If you find Faster R-CNN useful in your research, please consider citing:
Contents
Requirements: software
- Requirements for
Caffe
andpycaffe
(see: Caffe installation instructions)
Note: Caffe must be built with support for Python layers!
You can download my Makefile.config for reference.2. Python packages you might not have:
cython
, python-opencv
, easydict
3. [Optional] MATLAB is required for official PASCAL VOC evaluation only. The code now includes unofficial Python evaluation code.Requirements: hardware
- For training smaller networks (ZF, VGG_CNN_M_1024) a good GPU (e.g., Titan, K20, K40, ...) with at least 3G of memory suffices
- For training Fast R-CNN with VGG16, you'll need a K40 (~11G of memory)
- For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)
Installation (sufficient for the demo)
- Clone the Faster R-CNN repository
- We'll call the directory that you cloned Faster R-CNN into
FRCN_ROOT
Ignore notes 1 and 2 if you followed step 1 above.Note 1: If you didn't clone Faster R-CNN with the--recursive
flag, then you'll need to manually clone thecaffe-fast-rcnn
submodule:Note 2: Thecaffe-fast-rcnn
submodule needs to be on thefaster-rcnn
branch (or equivalent detached state). This will happen automatically if you followed step 1 instructions. - Build the Cython modules
- Build Caffe and pycaffe
- Download pre-computed Faster R-CNN detectorsThis will populate the
$FRCN_ROOT/data
folder withfaster_rcnn_models
. Seedata/README.md
for details.These models were trained on VOC 2007 trainval.
Demo
After successfully completing basic installation, you'll be ready to run the demo.
To run the demo
The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007.
Beyond the demo: installation for training and testing models
- Download the training, validation, test data and VOCdevkit
- Extract all of these tars into one directory named
VOCdevkit
- It should have this basic structure
- Create symlinks for the PASCAL VOC datasetUsing symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.
- [Optional] follow similar steps to get PASCAL VOC 2010 and 2012
- [Optional] If you want to use COCO, please see some notes under
data/README.md
- Follow the next sections to download pre-trained ImageNet models
Download pre-trained ImageNet models
Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16.
VGG16 comes from the Caffe Model Zoo, but is provided here for your convenience.ZF was trained at MSRA.
Usage
To train and test a Faster R-CNN detector using the alternating optimization algorithm from our NIPS 2015 paper, use
experiments/scripts/faster_rcnn_alt_opt.sh
.Output is written underneath $FRCN_ROOT/output
.('alt opt' refers to the alternating optimization training algorithm described in the NIPS paper.)
To train and test a Faster R-CNN detector using the approximate joint training method, use
experiments/scripts/faster_rcnn_end2end.sh
.Output is written underneath $FRCN_ROOT/output
.This method trains the RPN module jointly with the Fast R-CNN network, rather than alternating between training the two. It results in faster (~ 1.5x speedup) training times and similar detection accuracy. See these slides for more details.
Artifacts generated by the scripts in
tools
are written in this directory.Trained Fast R-CNN networks are saved under:
Test outputs are saved under: