dreadlord1984 / mobilenet-ssd

Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

MobileNet-SSD

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

Network	mAP	Download	Download
MobileNet-SSD	72.7	train	deploy

Run

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.

Train your own dataset

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 model.
4. Download the MobileNetSSD_train.caffemodel 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 deploy caffemodel.

About some details

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)].