SSD: Single Shot MultiBox Object Detector

SSD is an unified framework for object detection with a single network.

This is an implementation for the final course project of image preprocessing.

Our implementation is based on the ssd exmaple in mxnet, we modify some code to use our algorithms.

You can use the code to train/evaluate/test for sign detection task.

Disclaimer

This is a re-implementation of original SSD which is based on caffe. The official repository is available here. The arXiv paper is available here.

This example is intended for reproducing the nice detector while fully utilize the remarkable traits of MXNet.

The model is fully compatible with caffe version.

Here are some results on images from internet.

Demo results

mAP

Model	Training data	Test data	mAP
VGG16_reduced 300x300	VOC07+12 trainval	VOC07 test	71.57

Speed

Model	GPU	CUDNN	Batch-size	FPS*
VGG16_reduced 300x300	TITAN X(Maxwell)	v5.1	16	95
VGG16_reduced 300x300	TITAN X(Maxwell)	v5.1	8	95
VGG16_reduced 300x300	TITAN X(Maxwell)	v5.1	1	64
VGG16_reduced 300x300	TITAN X(Maxwell)	N/A	8	36
VGG16_reduced 300x300	TITAN X(Maxwell)	N/A	1	28

Forward time only, data loading and drawing excluded.

Getting started

You will need python modules: easydict, cv2, matplotlib and numpy. You can install them via pip or package managers, such as apt-get:

sudo apt-get install python-opencv python-matplotlib python-numpy
sudo pip install easydict

Build MXNet: Follow the official instructions

# for Ubuntu/Debian
cp make/config.mk ./config.mk

Remember to enable CUDA if you want to be able to train, since CPU training is insanely slow. Using CUDNN is optional.

Train the model

This example only covers training on sign dataset. Other datasets should be easily supported by adding subclass derived from class Imdb in dataset/imdb.py. See example of dataset/pascal_voc.py for details.

Train from scratch

By default, this example will use batch-size=32 and learning_rate=0.001. You might need to change the parameters a bit if you have different configurations. Check python train.py --help for more training options. For example, if you have 2 GPUs, use:

# note that a perfect training parameter set is yet to be discovered for multi-GPUs
python train.py --dataset=sign --network=vgg16_reduced --resume=-1  --finetune=0 --pretrained=''  --prefix=model/ssd300_vgg16_5_mobile  --batch-size=20 --gpus=0

Memory usage: MXNet is very memory efficient, training on VGG16_reduced model with batch-size 32 takes around 4684MB without CUDNN.
Initial lenarning rate: 0.001 is fine for single GPU. 0.0001 should be used for the first couple of epoches then go back to 0.001 via using parameter --resume.

Evalute trained model

Again, currently we only support evaluation on PASCAL VOC Use:

# cd /path/to/mxnet/example/ssd
python evaluate.py --gpus 0,1 --batch-size 128 --epoch 0

Convert model to deploy mode

This simply removes all loss layers, and attach a layer for merging results and non-maximum suppression. Useful when loading python symbol is not available.

# cd /path/to/mxnet/example/ssd
python deploy.py --num-class 20

BingzheWu/ssd-sign