Optimizing a CoreML model for real-time object detection on low-power iOS devices using Swift

Question

Background: I have experience working with Swift and CoreML, and I'm interested in creating a real-time object detection system that can run on iOS devices. I have a pre-trained CoreML model that I want to use for object detection, but I'm not sure how to optimize it for low-power devices like iPhones and iPads.

Specific Challenge: My main challenge is optimizing the object detection system for low-power devices without sacrificing accuracy or real-time performance. I want the system to be able to detect objects in real-time using the camera feed, but I also want to ensure that it doesn't drain the battery or cause performance issues.

Code Details: To load the pre-trained CoreML model, I used the following code:

import CoreML

// Load the pre-trained CoreML model
guard let model = try? VNCoreMLModel(for: MyObjectDetectionModel().model) else {
    fatalError("Failed to load CoreML model")
}

To set up the Vision request for object detection, I used the following code:

import Vision

// Create a Vision request for object detection
let objectDetectionRequest = VNCoreMLRequest(model: model) { request, error in
    guard let results = request.results as? [VNRecognizedObjectObservation] else {
        fatalError("Unexpected result type from VNCoreMLRequest")
    }
    
    // Process the object detection results
    // ...
}

// Set the request properties for object detection
objectDetectionRequest.imageCropAndScaleOption = .scaleFill

To process the object detection results, I used the following code:

// Process the object detection results
for result in results {
    // Extract the label and confidence score for each detected object
    let label = result.labels.first?.identifier ?? "Unknown"
    let confidence = result.labels.first?.confidence ?? 0.0
    
    // Create a rectangle for the detected object's bounding box
    let boundingBox = result.boundingBox
    
    // Do something with the label, confidence, and bounding box data
    // ...
}

To run the object detection system in real-time, I used the following code:

import AVFoundation

// Set up the camera capture session
let captureSession = AVCaptureSession()
captureSession.sessionPreset = .high

guard let captureDevice = AVCaptureDevice.default(for: .video) else {
    fatalError("Failed to get the camera device")
}

guard let input = try? AVCaptureDeviceInput(device: captureDevice) else {
    fatalError("Failed to create input device")
}

captureSession.addInput(input)

// Set up the video data output
let videoOutput = AVCaptureVideoDataOutput()
videoOutput.setSampleBufferDelegate(self, queue: DispatchQueue(label: "videoQueue"))
captureSession.addOutput(videoOutput)

// Start the capture session
captureSession.startRunning()

To optimize the CoreML model for low-power devices, I used the following code:

// Optimize the CoreML model for low-power devices
let optimizedModel = try? VNCoreMLModel(for: MyObjectDetectionModel().model, options: [.preferredMetalDevice: MTLCreateSystemDefaultDevice()])

if optimizedModel == nil {
    print("Failed to optimize CoreML model for low-power devices")
}

Questions:

1. How can I optimize a pre-trained CoreML model for low-power devices in order to improve real-time performance and reduce battery drain?
2. Are there any best practices for designing real-time object detection systems that are optimized for low-power devices, particularly on iOS?