I have been attempting to debug this for over 10 hours... I am working on implementing Apple's MobileNetV2 CoreML model into a Swift Playgrounds. I performed the following steps Compiled CoreML model in regular Xcode project Moved Compiled CoreML (MobileNetV2.mlmodelc) model to Resources folder of Swift Playground Copy Paste the model class (MobileNetV2.swift) into the Sources folder of Swift Playground Use UIImage extensions to resize and convert UIImage into CVbuffer Implement basic code to run the model. However, every time I run this, it keeps giving me this error: MobileNetV2.swift:100: Fatal error: Unexpectedly found nil while unwrapping an Optional value From the automatically generated model class function: /// URL of model assuming it was installed in the same bundle as this class class var urlOfModelInThisBundle : URL { let bundle = Bundle(for: self) return bundle.url(forResource: "MobileNetV2", withExtension:"mlmodelc")! } The model builds perfectly, this is my contentView Code: import SwiftUI struct ContentView: View { func test() -> String{ // 1. Load the image from the 'Resources' folder. let newImage = UIImage(named: "img") // 2. Resize the image to the required input dimension of the Core ML model // Method from UIImage+Extension.swift let newSize = CGSize(width: 224, height: 224) guard let resizedImage = newImage?.resizeImageTo(size: newSize) else { fatalError("⚠️ The image could not be found or resized.") } // 3. Convert the resized image to CVPixelBuffer as it is the required input // type of the Core ML model. Method from UIImage+Extension.swift guard let convertedImage = resizedImage.convertToBuffer() else { fatalError("⚠️ The image could not be converted to CVPixelBugger") } // 1. Create the ML model instance from the model class in the 'Sources' folder let mlModel = MobileNetV2() // 2. Get the prediction output guard let prediction = try? mlModel.prediction(image: convertedImage) else { fatalError("⚠️ The model could not return a prediction") } // 3. Checking the results of the prediction let mostLikelyImageCategory = prediction.classLabel let probabilityOfEachCategory = prediction.classLabelProbs var highestProbability: Double { let probabilty = probabilityOfEachCategory[mostLikelyImageCategory] ?? 0.0 let roundedProbability = (probabilty * 100).rounded(.toNearestOrEven) return roundedProbability } return("\(mostLikelyImageCategory): \(highestProbability)%") } var body: some View { VStack { let _ = print(test()) Image(systemName: "globe") .imageScale(.large) .foregroundColor(.accentColor) Text("Hello, world!") Image(uiImage: UIImage(named: "img")!) } } } Upon printing my bundle contents, I get these: ["_CodeSignature", "metadata.json", "__PlaceholderAppIcon76x76@2x~ipad.png", "Info.plist", "__PlaceholderAppIcon60x60@2x.png", "coremldata.bin", "{App Name}", "PkgInfo", "Assets.car", "embedded.mobileprovision"] Anything would help 🙏 For additional reference, here are my UIImage extensions in ExtImage.swift: //Huge thanks to @mprecke on github for these UIImage extension function. import Foundation import UIKit extension UIImage { func resizeImageTo(size: CGSize) -> UIImage? { UIGraphicsBeginImageContextWithOptions(size, false, 0.0) self.draw(in: CGRect(origin: CGPoint.zero, size: size)) let resizedImage = UIGraphicsGetImageFromCurrentImageContext()! UIGraphicsEndImageContext() return resizedImage } func convertToBuffer() -> CVPixelBuffer? { let attributes = [ kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue, kCVPixelBufferCGBitmapContextCompatibilityKey: kCFBooleanTrue ] as CFDictionary var pixelBuffer: CVPixelBuffer? let status = CVPixelBufferCreate( kCFAllocatorDefault, Int(self.size.width), Int(self.size.height), kCVPixelFormatType_32ARGB, attributes, &pixelBuffer) guard (status == kCVReturnSuccess) else { return nil } CVPixelBufferLockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0)) let pixelData = CVPixelBufferGetBaseAddress(pixelBuffer!) let rgbColorSpace = CGColorSpaceCreateDeviceRGB() let context = CGContext( data: pixelData, width: Int(self.size.width), height: Int(self.size.height), bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(pixelBuffer!), space: rgbColorSpace, bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue) context?.translateBy(x: 0, y: self.size.height) context?.scaleBy(x: 1.0, y: -1.0) UIGraphicsPushContext(context!) self.draw(in: CGRect(x: 0, y: 0, width: self.size.width, height: self.size.height)) UIGraphicsPopContext() CVPixelBufferUnlockBaseAddress(pixelBuffer!, CVPixelBufferLockFlags(rawValue: 0)) return pixelBuffer } }

I'm exploring my Vision Pro and finding it unclear whether I can even achieve things like body pose detection etc. https://developer.apple.com/videos/play/wwdc2023/111241/ It's clear that I can apply it to self provided images, but how about to the data coming from visionOS SDKs? All I can find is this mesh data from ARKit, https://developer.apple.com/documentation/arkit/arkit_in_visionos - am I missing something or do we not yet have good APIs for this? Appreciate any guidance! Thanks.

Hello, My understanding of the paper below is that iOS ships with a MobileNetv3-based ML model backbone, which then uses different heads for specific tasks in iOS. I understand that this backbone is accessible for various uses through the Vision framework, but I was wondering if it is also accessible for on-device fine-tuning for other purposes. Just as an example, if I want to have a model to detect some unique object in a photo, can I use the built in backbone or do I have to include my own in the app. Thanks very much for any advice and apologies if I didn't understand something correctly. Source: https://machinelearning.apple.com/research/on-device-scene-analysis

Hello Apple Developer community, I hope this message finds you well. I am currently facing an issue with Create ML in Xcode, and I am seeking assistance from the knowledgeable members of this forum. Any help or guidance would be greatly appreciated. Problem Description: I am encountering an unexpected issue when attempting to create a classification model for images using Create ML in Xcode. Upon opening Create ML, the application closes unexpectedly when I choose to create a new image classification model. Steps I Have Taken: I have already tried the following steps to troubleshoot the issue: Updated Xcode and macOS to the latest versions. Restarted Xcode and my computer. Created a new sample project to isolate the issue. Despite these efforts, the problem persists. System Information: Xcode Version: 15.2 macOS Version: Sonoma 14.0 I am on a tight deadline for a project, and resolving this issue quickly is crucial. Your help is invaluable, and I thank you in advance for any support you can provide. Best regards.

Apple Developer community, I recently updated Xcode and Core ML from version 13.0.1 to 14.1.2 and am facing an issue with the MLOneHotEncoder in my Core ML classifier. The same code and data that worked fine in the previous version now throw an error during predictions. The error message is: MLOneHotEncoder: unknown category String [TERM] expected one of This seems to suggest that the MLOneHotEncoder is not handling unknown strings, as it did in the previous version. Here's a brief overview of my situation: Core ML Model: The model is a classifier that uses MLOneHotEncoder for processing categorical data. Data: The same dataset is used for training and predictions, which worked fine before the update. Error Context: The error occurs at the prediction stage, not during training. I have checked for data consistency and confirmed that the dataset is the same as used with the previous version. Here are my questions: Has there been a change in how MLOneHotEncoder handles unknown categories in Core ML version 14.1.2? Are there any recommended practices for handling unknown string categories with MLOneHotEncoder in the updated Core ML version? Is there a need to modify the model training code or data preprocessing steps to accommodate changes in the new Core ML version? I would appreciate any insights or suggestions on how to resolve this issue. If additional information is needed, I am happy to provide it. Thank you for your assistance!