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.
5. You can run merge_bn.py to generate a no bn model, it will be much faster.

Create LMDB for your own dataset

1. Place the Images directory and Labels directory into same directory. (Each image in Images folder should have a unique label file in Labels folder with same name)
2. cd create_lmdb/code
3. Modify the labelmap.prototxt file according to your classes.
4. Modify the paths and directories in create_list.sh and create_data.sh as specified in same file in comments.
5. run bash create_list.sh, which will create trainval.txt, test.txt and test_name_size.txt
6. run bash create_data.sh, which will generate the LMDB in Dataset directory.
7. Delete trainval.txt, test.txt, test_name_size.txt before creation of next LMDB.

• LMDB Creation part is taken from https://github.com/jinfagang/kitti-ssd

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 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

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 are [(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)].

Reproduce the result

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.

Mobile Platform

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