Caffe implementation of SSD detection on MobileNetv2, converted from tensorflow.

0. Firstly you should download the original model from tensorflow.
1. Use gen_model.py to generate the train.prototxt and deploy.prototxt (or use the default prototxt).

python gen_model.py -s train -l labelmap_coco.prototxt -d trainval_lmdb -c 91 >train.prototxt
python gen_model.py -s deploy -l labelmap_coco.prototxt -d trainval_lmdb -c 91 >deploy.prototxt

2. Use dump_tensorflow_weights.py to dump the weights of conv layer and batchnorm layer.
3. Use load_caffe_weights.py to load the dumped weights to deploy.caffemodel.
4. Use the code in src to accelerate your training if you have a cudnn7, or add "engine: CAFFE" to your depthwise convolution layer to solve the memory issue.
5. Maybe you need deploy.caffemodel for VOC dataset, use coco2voc.py to get it.

There are some differences between caffe and tensorflow implementation:

1. The padding method 'SAME' in tensorflow sometimes use the [0, 0, 1, 1] paddings, means that top=0, left=0, bottom=1, right=1 padding. In caffe, there is no parameters can be used to do that kind of padding.
2. MobileNet on Tensorflow use ReLU6 layer y = min(max(x, 0), 6), but caffe has no ReLU6 layer.

The original/deploy.prototxt shows a exactly same version with tensorflow, and you can use my tensorflow-compatible ssd to see the result. After that, I will do some finetuning on the converted model to make it work on original ssd in the next few days.