NOTICE: This repo is no longer maintained. For an easy-to-use object detector that is actively maintained, I recommend considering the PyTorch Mask R-CNN implementation.
This directory contains code to train and evaluate the SSD object detector described in the paper:
SSD: Single Shot MultiBox Detector
by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy,
Scott Reed, Cheng-Yang Fu, Alexander C. Berg
The code is based on the caffe implementation
made available by Wei Liu.
Running the ssd_demo.m script will download a model trained on pascal voc 2007 data and run it on a sample image to produce the figure below:
The matconvnet training code aims to reproduce the results
achieved by the caffe training routine. Using the "zoom out"
data augmentation scheme described in the updated SSD paper
the model trained with matconvnet achieves
a similar mAP on the 2007 test set to the caffe model.
Test Set Results - comparison of the ssd-pascal-vggvd-300 model:
-------------- -------------------- ------------------------
trained with caffe trained with matconvnet
-------------- -------------------- ------------------------
aeroplane 80.53 82.39
bicycle 83.77 85.82
bird 76.40 77.40
boat 71.53 71.43
bottle 50.17 52.82
bus 86.90 86.54
car 86.05 86.20
cat 88.57 87.04
chair 59.96 60.07
cow 81.39 81.59
diningtable 76.30 75.57
dog 85.92 84.65
horse 86.60 86.65
motorbike 83.62 84.94
person 79.57 79.47
pottedplant 52.62 50.30
sheep 79.22 79.19
sofa 78.89 78.82
train 86.52 87.02
tvmonitor 76.31 77.15
-------------- -------------------- ------------------------
mean 77.54 77.75
-------------- -------------------- ------------------------
The public caffe models released by Wei Liu have been imported into
matconvnet for use. The MobileNet model released by chuanqi305
has also been imported. In addition, some sample models trained with the
matconvnet implementation have been made available. These can be
downloaded directly from
here (a few pre-trained
models will be downloaded automatically upon running the
core/ssd_pretrained_benchmarks.m script).
The pre-trained ssd-pascal-vggvd-300 model runs at approximately
58 Hz on a Tesla M-40.
CPU-mode:
matconvnet (tested with v1.0-beta23, v1.0-beta24)MATLAB (tested with 2016a)
additional GPU-mode dependency:
CUDA (tested with v7.5, v8)
mcnSSD also requires the following two modules:
- autonn - automatic differenation
- mcnExtraLayers - extra MatConvNet layers
Both of these can be setup directly with vl_contrib (i.e. run vl_contrib install <module-name> then vl_contrib setup <module-name>).
The easiest way to use this module is to install it with the vl_contrib
package manager. mcnSSD can be installed with
the following commands from the root directory of your MatConvNet
installation:
vl_contrib('install', 'mcnSSD') ;
vl_contrib('compile', 'mcnSSD') ;
vl_contrib('setup', 'mcnSSD') ;
vl_contrib('test', 'mcnSSD') ; % optional
The ssd_demo.m script gives an example of how to run a pre-trained model
on a single image. The core/ssd_pretrained_benchmarks.m will download
and evaluate a range of pre-trained SSD models on the Pascal VOC 2007 test
set.
An example of model training can be found in pascal/ssd_pascal_train.m
- If you get the following error:
Undefined function or variable 'vl_argparsepos', it indicates that autonn is not on your path. It can be added by runningvl_contrib install autonn ; vl_contrib setup autonn ;from the root of your MatConvNet install.
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