SSD: Single Shot MultiBox Object Detector

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

You can use the code to train/evaluate/test for object 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. * Model converter from caffe is available now! * The result is almost identical to the original version. However, due to different implementation details, the results might differ slightly.

Due to the permission issue, this example is maintained in this repository separately. You can use the link regarding specific per example issues.

What's new

Added multiple trained models.
Added a much simpler way to compose network from mainstream classification networks (resnet, inception...) and Guide.
Update to the latest version according to caffe version, with 5% mAP increase.
Use C++ record iterator based on back-end multi-thread engine to achieve huge speed up on multi-gpu environments.
Monitor validation mAP during training.
More network symbols under development and test.
Extra operators are now in mxnet/src/operator/contrib.
Old models are incompatible, use e06c55d or e4f73f1 for backward compatibility. Or, you can modify the json file to update the symbols if you are familiar with it, because only names have changed while weights and bias should still be good.