Single Shot MultiBox Detector implemented by Keras

github/wikke

Introduction

SSD(Single Shot MultiBox Detector) is a state-of-art object detection algorithm, brought by Wei Liu and other wonderful guys, see SSD: Single Shot MultiBox Detector @ arxiv, recommended to read for better understanding.

Also, SSD currently performs good at PASCAL VOC Challenge, see http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=3

References

My work is just playing with this fantastic algorithm, and see the detection result of my own. Many many thanks goes to rykov8/ssd_keras, which is an excellent implementation. cory8249/ssd_keras pulled an request, which makes it works with Keras 2 version, saves me a lot of time. Core implementation code comes from rykov8/ssd_keras

Additionally, I would like to make the project more simple and clear for understanding. Yes, it takes me time to figure out the mainline. So a little re-construction is done, leaving the function name same for the sake of easy comparison with original repo.

Guides

The code structures looks like below:

- SSD.ipynb # Main code
- generator.py  # customrized generator, which return proper training data structure
				# including image and assigned boxes(similar to input boxex)
- get_data_from_XML.py # parse Annotations of PASCAL VOC, helper of generator
- VOCdevkit # dataset downloaded from [http://host.robots.ox.ac.uk/pascal/VOC/, use The VOC2007 Challenge in this example

- SSD300 # SSD 300 model lib
	- ssd_v2.py # main model
	- ssd_layers.py # Normalize and PriorBox defenition
	- ssd_training.py # MultiboxLoss Definition
	- ssd_utils.py # Utilities including encode,decode,assign_boxes
	- prior_boxes_ssd300.pkl # my understanding is the model pre-defined static prior boxes

SSD

network structure

Loss function/ Training Objectives

Resources

After hours of training on Aliyun-GN4-instance with Nvidia M40 GPU, I got result with multiple object categorical loss 3.3392 at 16th epoch. Weights can be downloaded at https://drive.google.com/file/d/0B5o_TPhUdyJWWEl5WG1lcUxCZzQ/view?usp=sharing