# 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](https://drive.google.com/file/d/0B3gersZ2cHIxVFI1Rjd5aDgwOG8/view?usp=sharing&resourcekey=0-06X5rxeA-Wg6pg3UIeF5og)|[deploy](https://drive.google.com/file/d/0B3gersZ2cHIxRm5PMWRoTkdHdHc/view?usp=sharing&resourcekey=0-1Lpfs4EvGDeCQz12AF64hQ)

### Run
1. Download [SSD](https://github.com/weiliu89/caffe/tree/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](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md):
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](https://drive.google.com/open?id=0B3gersZ2cHIxZi13UWF0OXBsZzA) and [prototxt](https://drive.google.com/open?id=0B3gersZ2cHIxWGEzbG5nSXpNQzA)) converted from [tensorflow](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz). 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](https://github.com/chuanqi305/rscnn).