PDCamera iOS Demo with SSD Model


This iOS demo shows PaddlePaddle running SSD(Single Shot MultiBox Detector)Object detection on iOS devices locally and offline. It loads a pretrained model with PaddlePaddle and uses camera to capture images and call PaddlePaddle's inference ability to show detected objects to users.

You can look at SSD model architecture here and a linux demo here

Pre-trained Models

pascal_mobilenet_300_66 and vgg_ssd_net models can classify 20 objects. face_mobilenet_160_91 can only classify human's face.

Model Dimensions Accuracy Size
pascal_mobilenet_300_66.paddle 300 x 300 66% 23.2MB
vgg_ssd_net.paddle 300 x 300 71% 104.3MB
face_mobilenet_160_91.paddle 160 x 160 91% 18.4MB

Demo Screenshot

Simply tap on the screen to toggle settings.

  • Models: Select Pascal MobileNet 300 or Face MobileNet 160, App will exit, need to launch to restart.
  • Camera: Toggle Front/Back Camera. App will exit, need to launch to restart.
  • Accuracy Threshold: Adjust threshold to filter more/less objects based on probability.
  • Time Refresh Rate: Adjust the time to refresh bounding box more/less frequently.


Detected object will be highlighted as a bounding box with a classified object label and probability.

Fast Installation through QR Code

To simply run the demo with iPhone/iPad, scan the QR code below, click "Install PDCamera" in the link and the app will be downloaded in the background. After installed, go to Settings -> General -> Device Management -> Baidu USA llc -> Trust "Baidu USA llc"

Build from Source Code

Use latest XCode for development. This demo requires a camera for object detection, therefore you must use a device (iPhone or iPad) for development and testing. Simulators will not work as they cannot access camera.

For developers, feel free to use this as a reference to start a new project. This demo fully demonstrates how to integrate Paddle C Library to iOS and called from Swift.

Swift cannot directly call C API, in order to have client in Swift work, create Objective-C briding header and a Objective-C++ wrapper (.mm files) to access paddle APIs.

Prepare Models

Our models are too large to upload to Github. Create a model folder and add to project root. Download face_mobilenet_160_91.paddle and pascal_mobilenet_300_66.paddle to the model folder.

(Optional) VGG model is relatively large and takes much higher memory(~800Mb), power, and much slower (~1.5secs) on each inference but it has slightly accuracy gain (See below section) Note: Only runs on iPhone6s or above (iPhone 6 or below will crash due to memory limit) If you want to try it out, download vgg_ssd_net.paddle, then go to XCode target -> Bulid Phases -> Copy Bundle Resources, click '+' to add vgg_ssd_net.paddle

Prepare PaddlePaddle Inference Library

Follow this guide Build PaddlePaddle for iOS to generate paddle libs(include, lib, third_party). Create a folder paddle-ios and add to project root. Put the 3 paddle libs folder under paddle-ios.

Directory Tree

$ git clone https://github.com/PaddlePaddle/Mobile.git
$ cd Mobile/Demo/iOS/AICamera
$ tree
├── AICamera  # sources codes
├── PDCamera.xcodeproj
├── README.md
├── README_cn.md
├── assets
├── models  # models
│   ├── face_mobilenet_160_91.paddle
│   ├── pascal_mobilenet_300_66.paddle
│   └── vgg_ssd_net.paddle
└── paddle-ios  # PaddlePaddle inference library
    ├── include
    ├── lib
    │   ├── libpaddle_capi_engine.a
    │   ├── libpaddle_capi_layers.a
    │   └── libpaddle_capi_whole.a
    └── third_party

Integrate Paddle C Library to iOS Project

  • Add the include directory to Header Search Paths

  • Add the Accelerate.framework or veclib.framework to your project, if your PaddlePaddle is built with IOS_USE_VECLIB_FOR_BLAS=ON
  • Add the libraries of paddle, libpaddle_capi_layers.a and libpaddle_capi_engine.a, and all the third party libraries to your project

  • Set -force_load for libpaddle_capi_layers.a