AIML-Board App

AIML-Board Application

What we Aim?

To develop a machine learning model for object detection (Pedestrians, Cars, Motorbike, Bicycle, Bus) which is supported by Snapdragon mobile platforms with the help of Qualcomm Neural Processing SDK.

The Machine learning model to detect the Objects uses Single Shot Detector (SSD) algorithm trained on Mobilenet network architecture.

Why MobilenetSSD

• Gives realtime inference frame rates 8-13 FPS on HDK835
• Better performance and accuracy compared to other architectures like YOLO Etc,

How to train the model

Installation of MobilenetSSD and Caffe

1.Clone the caffe source code from the below git repository using below commands.

$ git clonehttps://github.com/weiliu89/caffe.git
$ cd caffe
$ git checkout ssd

2.Depending on the Processor used either CPU or GPU install the depending packages by executing respective instructions from http://caffe.berkeleyvision.org/install_apt.html

3.Build caffe using the below instructions

$ cp Makefile.config.example Makefile.config

(Make necessary modification in the Makefile as per the deviceconfiguration)

$ make -j8

(Make sure you add the $CAFFE_ROOT/python to your PYTHON PATH)

Getting the data ready

1.Download fully convolution reduced(atrous) VGGNet by following the given instructions

$ git clonehttps://gist.github.com/2ed6e13bfd5b57cf81d6.git
$mv 2ed6e13bfd5b57cf81d6  $CAFFE_ROOT/models/VGGNet/

2.Download the VOC2007 and VOC2012 dataset,

$ mkdir home/<username>/data
$ cd home/<username>/data
$ wgethttp://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
$ wgethttp://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
$ wgethttp://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
$ tar -xvf VOCtrainval_11-May-2012.tar
$ tar -xvf VOCtrainval_06-Nov-2007.tar
$ tar -xvf VOCtest_06-Nov-2007.tar

3.Creating the LightningMemory-Mapped Database( LMDB ) file

$ cd $CAFFE_ROOT
$ ./data/VOC0712/create_list.sh
$ ./data/VOC0712/create_data.sh

Training the model

1.Creating the symbolic link to training and test data sets,

$ ln -s PATH_TO_YOUR_TRAIN_LMDB trainval_lmdb
$ ln -s PATH_TO_YOUR_TEST_LMDB test_lmdb

2.Copy a labelmap_voc.prototxt file from ssd repo tree to MobileNetSSD Dir using below command,

$ cp $CAFFE_DIR/data/VOC0712/labelmap_voc.prototxt$MOBILENETSSD_DIR/labelmap.prototxt

Run gen_model.sh for generating the training & testing prototxt with given number of classes. Please make sure that number of classes while running gen_model.sh & number of classes mentioned in labelmap.prototxt should match.

$ ./gen_model.sh 21

Now train your model using train.sh, and keep on training unless loss is in between 1.5 to 2.5.

$ ./train.sh

Test the trained model with test.sh script.

$ ./test.sh

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

once merge_bn.py has run successfully, you’ll get two files in the root directory of MobileNetSSD. These are our trained models,

• no_bn.prototxt
• no_bn.caffemodel.

How to Convert Caffe into DLC?

Prerequisites: Neural Processing SDK setup. Use the instruction in from the below link to make the setup,

https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk/getting-started

Initialize the environmental variables of Neural Processing SDK with caffe.For converting the model from caffe to dlc, you need 2 files, prototxt and caffemodel file. Once you’ve both convert it into dlc using the following command:

$ snpe-caffe-to-dlc --caffe_txt MobileNetSSD_deploy.prototxt--caffe_bin MobileNetSSD_deploy.caffemodel --dlccaffe_mobilenet_ssd.dlc