A caffe implementation of MobileNet-SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

1. Download SSD source code and compile (follow the SSD README).
2. Download the pretrained deploy weights from the link above.
3. Put all the files in SSD_HOME/examples/
4. Run demo.py to show the detection result.
5. You can run merge_bn.py to generate a no bn model, it will be much faster.

1. Convert your own dataset to lmdb database (follow the SSD README), and create symlinks to current directory.

ln -s PATH_TO_YOUR_TRAIN_LMDB trainval_lmdb
ln -s PATH_TO_YOUR_TEST_LMDB test_lmdb

2. Create the labelmap.prototxt file and put it into current directory.
3. Use gen_model.sh to generate your own training prototxt.
4. Download the training weights from the link above, and run train.sh, after about 30000 iterations, the loss should be 1.5 - 2.5.
5. Run test.sh to evaluate the result.
6. Run merge_bn.py to generate your own no-bn caffemodel if necessary.

python merge_bn.py --model example/MobileNetSSD_deploy.prototxt --weights snapshot/mobilenet_iter_xxxxxx.caffemodel

There are 2 primary differences between this model and MobileNet-SSD on tensorflow:

1. ReLU6 layer is replaced by ReLU.
2. For the conv11_mbox_prior layer, the anchors is [(0.2, 1.0), (0.2, 2.0), (0.2, 0.5)] vs tensorflow's [(0.1, 1.0), (0.2, 2.0), (0.2, 0.5)].

I trained this model from a MobileNet classifier(caffemodel and prototxt) converted from tensorflow. I first trained the model on MS-COCO and then fine-tuned on VOC0712. Without MS-COCO pretraining, it can only get mAP=0.68.

You can run it on Android with my another project rscnn.

从新训练mobilenet-ssd模型 1.不要用gen_model.sh生成的MobileNetSSD_deploy.prototxt和MobileNetSSD_test.prototxt， 这两个文件跟我们的MobileNetSSD_deploy.caffemodel(已经在用的,mAP：54.48%)有一些层名称不一样。

2.finetune的模型用自带的mobilenet_iter_73000.caffemodel， 这个是带bn层的，不要用我们的MobileNetSSD_deploy.caffemodel，这个是去掉了bn层的，不能用来重训练或者说最好不要用来重训练（训练的prototxt要去掉bn层，这样不好）

3.不带bn层的模型和deploy，相应的demo.py中要同时替换原先的netfile和weights文件 root@88dc478cc057:/home/root_work/MobileNet-SSD-new-szj/MobileNet-SSD# python merge_bn.py --model deploy.prototxt --weights snapshot/MobileNetSSD_iter_100.caffemodel

带bn层的话，demo.py中的netfile 用deploy.prototxt, 模型用直接生成的。