[yolov5] + [ios] + [tensorflow lite]

Overview

YOLOv5 - TensorFlow Lite Object Detection iOS Example Application

iOS Versions Supported: iOS 12.0 and above. Xcode Version Required: 10.0 and above

Overview

This is a camera app that continuously detects the objects (bounding boxes and classes) in the frames seen by your device's back camera, using a YOLOv5 model. These instructions walk you through building and running the demo on an iOS device.

Prerequisites

  • You must have Xcode installed

  • You must have a valid Apple Developer ID

  • The demo app requires a camera and must be executed on a real iOS device. You can build it and run with the iPhone Simulator but the app raises a camera not found exception.

  • You don't need to build the entire TensorFlow library to run the demo, it uses CocoaPods to download the TensorFlow Lite library.

  • You'll also need the Xcode command-line tools: xcode-select --install If this is a new install, you will need to run the Xcode application once to agree to the license before continuing.

Building the iOS Demo App

  1. Install CocoaPods if you don't have it. sudo gem install cocoapods

  2. Install the pod to generate the workspace file: cd yolov5-ios-tensorflow-lite/ pod install If you have installed this pod before and that command doesn't work, try pod update At the end of this step you should have a file called ObjectDetection.xcworkspace

  3. Open ObjectDetection.xcworkspace in Xcode.

  4. Please change the bundle identifier to a unique identifier and select your development team in 'General->Signing' before building the application if you are using an iOS device.

  5. Build and run the app in Xcode. You'll have to grant permissions for the app to use the device's camera. Point the camera at various objects and enjoy seeing how the model classifies things!

Model Used

This app uses a YOLOv5 model.

To use YOLOv5's custom training model, convert it to a tflite model through export.py in the YOLOv5 repository.

export.py --weights your_model.pt --include tflite

iOS App Details

The app is written entirely in Swift and uses the TensorFlow Lite Swift library for performing object detection.

The app is built using sample code from the TensorFlow repository.

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Comments
  • Run script permission denied

    Run script permission denied

    Hi, thanks for sharing this code. I was trying to build in xcode but got this permission denied error..

    olov5-ios-tensorflow-lite/RunScripts/download_models.sh: Permission denied Command PhaseScriptExecution failed with a nonzero exit code

    Do you know how to solve this problem?

    opened by jaehyunshinML 2
Releases(v1.0.0)
  • v1.0.0(Dec 23, 2021)

    YOLOv5 - TensorFlow Lite Object Detection iOS Example Application

    iOS Versions Supported: iOS 12.0 and above. Xcode Version Required: 10.0 and above

    Overview

    This is a camera app that continuously detects the objects (bounding boxes and classes) in the frames seen by your device's back camera, using a YOLOv5 model. These instructions walk you through building and running the demo on an iOS device.

    Prerequisites

    • You must have Xcode installed

    • You must have a valid Apple Developer ID

    • The demo app requires a camera and must be executed on a real iOS device. You can build it and run with the iPhone Simulator but the app raises a camera not found exception.

    • You don't need to build the entire TensorFlow library to run the demo, it uses CocoaPods to download the TensorFlow Lite library.

    • You'll also need the Xcode command-line tools: xcode-select --install If this is a new install, you will need to run the Xcode application once to agree to the license before continuing.

    Building the iOS Demo App

    1. Install CocoaPods if you don't have it. sudo gem install cocoapods

    2. Install the pod to generate the workspace file: cd yolov5-ios-tensorflow-lite/ pod install If you have installed this pod before and that command doesn't work, try pod update At the end of this step you should have a file called ObjectDetection.xcworkspace

    3. Open ObjectDetection.xcworkspace in Xcode.

    4. Please change the bundle identifier to a unique identifier and select your development team in 'General->Signing' before building the application if you are using an iOS device.

    5. Build and run the app in Xcode. You'll have to grant permissions for the app to use the device's camera. Point the camera at various objects and enjoy seeing how the model classifies things!

    Model Used

    This app uses a YOLOv5 model.

    To use YOLOv5's custom training model, convert it to a tflite model through export.py in the YOLOv5 repository.

    export.py --weights your_model.pt --include tflite

    iOS App Details

    The app is written entirely in Swift and uses the TensorFlow Lite Swift library for performing object detection.

    The app is built using sample code from the TensorFlow repository.

    Source code(tar.gz)
    Source code(zip)
Owner
Inpyo Hong
Computer Vision / Deep Learning Engineer
Inpyo Hong
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