How to convert .svm trained model to coreML .mlmodel ?

I'm bad at English... I'm using create ML. I'm using X code. The csv file cannot be read. The same thing happens with core ML API. The structure of csv is as follows. ** text,labelA,labelB,labelC,labelD hello,2,3,4,5 good,3,4,5,6 ** I tried some. I don't think the structure is wrong... I think it's a setting problem. Please help me.

Hello, I've tried to label keypoints of my data by labelme, VGG, Make Sense... etc. But their output of annotation json or csv file did not work for createML. Is there any other tool or conversion method ? By the way, I also did not find any demo format of keypoints json file. Does anyone know? Thanks!

I would like to generate and run ML program inside an app. I got familiar with the coremltools and MIL format, however I can't seem to find any resources on how to generate mlmodel/mlpackage files using Swift on the device. Is there any Swift equivalent of coremltools? Or is there a way to translate MIL description of a ML program into instance of a MLModel? Or something similar.

I am trying to build an app that uses CoreML. However, I would like the data that was used to build the model to grow and the model to predict taking that growth into account. So, at the end of the day the more the user uses the app the smarter the app gets at predicting what the user will select next. For example: If the user is presented with a variety of clothes to choose from and the user selects pants, the app will present a list of colors to choose from and let's say the user chooses blue, the next time the user chooses pants the blue color is ranked higher than it was the previous time. Is this possible to do? And how do I make selection updates? Thanks in advance for any ideas or suggestions.

Is there anyway we can set the number of threads used during coreML inference? My model is relatively small and the overhead of launching new threads is too expensive. When using TensorFlow C API, forcing to single thread results in significant decrease in CPU usage. (So far coreML with multiple threads has 3 times the cpu usage compares to TensorFlow with single thread). Also, wondering if anyone has compared the performance between TensorFlow in C and coreML?

Hello, I have an object detection model that I integrated into an app. When I put an image on the preview for the Object Detection File, it classifies the image correctly. However, if I put the same image onto the app, it classifies it differently with different values. I am confused as to how this is happening. Here is my code: import UIKit import CoreML import Vision import ImageIO class SecondViewController: UIViewController, UINavigationControllerDelegate { @IBOutlet weak var photoImageView: UIImageView! lazy var detectionRequest: VNCoreMLRequest = { do { let model = try VNCoreMLModel(for: EarDetection2().model) let request = VNCoreMLRequest(model: model, completionHandler: { [weak self] request, error in self?.processDetections(for: request, error: error) }) request.imageCropAndScaleOption = .scaleFit return request } catch { fatalError("Failed to load Vision ML model: \(error)") } }() @IBAction func testPhoto(_ sender: UIButton) { let vc = UIImagePickerController() vc.sourceType = .photoLibrary vc.delegate = self present(vc, animated: true) } @IBOutlet weak var results: UILabel! func updateDetections(for image: UIImage) { let orientation = CGImagePropertyOrientation(rawValue: UInt32(image.imageOrientation.rawValue)) guard let ciImage = CIImage(image: image) else { fatalError("Unable to create \(CIImage.self) from \(image).") } DispatchQueue.global(qos: .userInitiated).async { let handler = VNImageRequestHandler(ciImage: ciImage, orientation: orientation!) do { try handler.perform([self.detectionRequest]) } catch { print("Failed to perform detection.\n\(error.localizedDescription)") } } } func processDetections(for request: VNRequest, error: Error?) { DispatchQueue.main.async { guard let results = request.results else { print("Unable to detect anything.\n\(error!.localizedDescription)") return } let detections = results as! [VNRecognizedObjectObservation] self.drawDetectionsOnPreview(detections: detections) } } func drawDetectionsOnPreview(detections: [VNRecognizedObjectObservation]) { guard let image = self.photoImageView?.image else { return } let imageSize = image.size let scale: CGFloat = 0 UIGraphicsBeginImageContextWithOptions(imageSize, false, scale) for detection in detections { image.draw(at: CGPoint.zero) print(detection.labels.map({"\($0.identifier) confidence: \($0.confidence)"}).joined(separator: "\n")) print("------------") results.text = (detection.labels.map({"\($0.identifier) confidence: \($0.confidence)"}).joined(separator: "\n")) // The coordinates are normalized to the dimensions of the processed image, with the origin at the image's lower-left corner. let boundingBox = detection.boundingBox let rectangle = CGRect(x: boundingBox.minX*image.size.width, y: (1-boundingBox.minY-boundingBox.height)*image.size.height, width: boundingBox.width*image.size.width, height: boundingBox.height*image.size.height) UIColor(red: 0, green: 1, blue: 0, alpha: 0.4).setFill() UIRectFillUsingBlendMode(rectangle, CGBlendMode.normal) } let newImage = UIGraphicsGetImageFromCurrentImageContext() UIGraphicsEndImageContext() self.photoImageView?.image = newImage } } extension SecondViewController: UIImagePickerControllerDelegate { func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [UIImagePickerController.InfoKey : Any]) { picker.dismiss(animated: true) guard let image = info[.originalImage] as? UIImage else { return } self.photoImageView?.image = image updateDetections(for: image) } } I attached pictures of the model preview and the app preview (it may be hard to tell but they are the same image). I have also attached pictures of my files and storyboard. Any help would be great! Thanks in advance!

Create a sample project in Creat ML and choose Activity Classifier. When lowering the sample rate, the preview timeline gets shorter instead of getting longer. Furthermore, it seems the entire preview timeline breaks (can't scroll) if the sample rate is anything other than 50. Try it out: Train a model with sample rate 50, then try training it with sample rate 10.

DataFrame(contentsOfJSONFile:url) (and it's MLDataTable equivalent) assumes that the rows to be imported are at the root level of the JSON structure. However, I'm downloading a fairly large dataset that has "header" information, such as date created and validity period, with the targeted array at a subsidiary level. The DataFrame initialiser has JSONReadingOptions, but these don't apply to this situation (i.e. there's nothing that helps). It seems that I'm faced with the options of 1) stripping the extraneous data from the JSON file, to leave just the array or 2) decoding the JSON file into a bespoke struct then converting its array into a DataFrame - which removes a lot of the benefits of using DataFrame in the first place. Any thoughts? Cheers, Michaela

Hi, I try the activity classifier and want to show the results with an app on a IPhone SE. I try the example analog https://apple.github.io/turicreate/docs/userguide/activity_classifier/export_coreml.html When calling the prediction function: ...    func performModelPrediction () -> String? { ... EXC_BAD_ACCESS (code=1, address=0x0) was thrown in line:         let modelPrediction = try! activityClassificationModel.prediction( ^^^^^^^^here was the Error shown   I can trak it down to the source of the mlmodel:     func prediction(input: MyActivityClassifier2Input, options: MLPredictionOptions) throws -> MyActivityClassifier2Output {         let outFeatures = try model.prediction(from: input, options:options)         return MyActivityClassifier2Output(features: outFeatures)     } I created the model with ml creator, working with Xcode 13 beta. What I am doing wrong ? Do you have any hints ? Is there a better example for activity classifier ? Don't hesitate to ask for further details. -Hans here my code: // //  ViewController.swift //  motionstor4yboard // //  Created by Hans Regler on 19.07.21. // import Foundation import UIKit import CoreML import CoreMotion class ViewController: UIViewController {     override func viewDidLoad() {         debugPrint("info: start viewDidLoad ... ")         super.viewDidLoad()         // Do any additional setup after loading the view, typically from a nib.                 // Connect data:         self.startDeviceMotion()     }      // Define some ML Model constants for the recurrent network   struct ModelConstants {     static let numOfFeatures = 6     // Must be the same value you used while training     static let predictionWindowSize = 100     static let sensorsUpdateInterval = 1.0 / 10.0     static let stateInLength = 400   }      // Initialize the model, layers, and sensor data arrays     let activityClassificationModel = MyActivityClassifier2()     var currentIndexInPredictionWindow = 0     let accelDataX = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let accelDataY = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let accelDataZ = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let gyroDataX = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let gyroDataY = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     let gyroDataZ = try! MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double)     var stateOutput = try! MLMultiArray(shape:[ModelConstants.stateInLength as NSNumber], dataType: MLMultiArrayDataType.double)     // Initialize CoreMotion Manager   let motionManager = CMMotionManager()      func startDeviceMotion() { //         guard motionManager.isDeviceMotionAvailable else {     guard motionManager.isAccelerometerAvailable, motionManager.isGyroAvailable else {             debugPrint("Core Motion Data Unavailable!")             return         }          motionManager.accelerometerUpdateInterval = TimeInterval(ModelConstants.sensorsUpdateInterval)     motionManager.gyroUpdateInterval = TimeInterval(ModelConstants.sensorsUpdateInterval)     motionManager.startAccelerometerUpdates(to: .main) { accelerometerData, error in         guard let accelerometerData = accelerometerData else {             print("Error: accelerometerData = accelerometerData")             return }         // Add the current data sample to the data array         self.addAccelSampleToDataArray(accelSample: accelerometerData)     }             }      func addAccelSampleToDataArray (accelSample: CMAccelerometerData) {     // Add the current accelerometer reading to the data array     accelDataX[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.x as NSNumber     accelDataY[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.y as NSNumber     accelDataZ[[currentIndexInPredictionWindow] as [NSNumber]] = accelSample.acceleration.z as NSNumber     // Update the index in the prediction window data array     currentIndexInPredictionWindow += 1     // If the data array is full, call the prediction method to get a new model prediction.     // We assume here for simplicity that the Gyro data was added to the data arrays as well.     if (currentIndexInPredictionWindow == ModelConstants.predictionWindowSize) {         if let predictedActivity = performModelPrediction() {             // Use the predicted activity here             // ...             // Start a new prediction window             currentIndexInPredictionWindow = 0         }     } }          func performModelPrediction () -> String? {         // Perform model prediction         let modelPrediction = try! activityClassificationModel.prediction(             acceleration_x: accelDataX,             acceleration_y: accelDataY,             acceleration_z: accelDataZ,             rotation_x: gyroDataX,             rotation_y: gyroDataY,             rotation_z: gyroDataZ,             stateIn: stateOutput)         // Update the state vector         stateOutput = modelPrediction.stateOut         // Return the predicted activity - the activity with the highest probability         return modelPrediction.label     }

Most examples, including within documentation, of using CoreML with iOS involve the creation of the Model under Xcode on a Mac and then inclusion of the Xcode generated MLFeatureProvider class into the iOS app and (re)compiling the app.  However, it’s also possible to download an uncompiled model directly into an iOS app  and then compile it (background tasks) - but there’s no MLFeatureProvider class.  The same applies when using CreateML in an iOS app (iOS 15 beta) - there’s no automatically generated MLFeatureProvider.  So how do you get one?  I’ve seen a few queries on here and elsewhere related to this problem, but couldn’t find any clear examples of a solution.  So after some experimentation, here’s my take on how to go about it: Firstly, if you don’t know what features the Model uses, print the model description e.g. print("Model: ",mlModel!.modelDescription). Which gives Model:   inputs: (     "course : String",     "lapDistance : Double",     "cumTime : Double",     "distance : Double",     "lapNumber : Double",     "cumDistance : Double",     "lapTime : Double" ) outputs: (     "duration : Double" ) predictedFeatureName: duration ............ A prediction is created by guard **let durationOutput = try? mlModel!.prediction(from: runFeatures) ** …… where runFeatures is an instance of a class that provides a set of feature names and the value of each feature to be used in making a prediction.  So, for my model that predicts run duration from course, lap number, lap time etc the RunFeatures class is: class RunFeatures : MLFeatureProvider {     var featureNames: Set = ["course","distance","lapNumber","lapDistance","cumDistance","lapTime","cumTime","duration"]     var course : String = "n/a"     var distance : Double = -0.0     var lapNumber : Double = -0.0     var lapDistance : Double = -0.0     var cumDistance : Double = -0.0     var lapTime : Double = -0.0     var cumTime : Double = -0.0          func featureValue(for featureName: String) -> MLFeatureValue? {         switch featureName {         case "distance":             return MLFeatureValue(double: distance)         case "lapNumber":             return MLFeatureValue(double: lapNumber)         case "lapDistance":             return MLFeatureValue(double: lapDistance)         case "cumDistance":             return MLFeatureValue(double: cumDistance)         case "lapTime":             return MLFeatureValue(double: lapTime)         case "cumTime":             return MLFeatureValue(double: cumTime)         case "course":             return MLFeatureValue(string: course)         default:             return MLFeatureValue(double: -0.0)         }     } } Then in my DataModel, prior to prediction, I create an instance of RunFeatures with the input values on which I want to base the prediction: var runFeatures = RunFeatures() runFeatures.distance = 3566.0 runFeatures.lapNumber = 1.0 runFeatures.lapDistance = 1001.0  runFeatures.lapTime = 468.0  runFeatures.cumTime = 468.0  runFeatures.cumDistance = 1001.0  runFeatures.course = "Wishing Well Loop" NOTE there’s no need to provide the output feature (“duration”) here, nor in the featureValue method above but it is required in featureNames. Then get the prediction with guard let durationOutput = try? mlModel!.prediction(from: runFeatures)  Regards, Michaela

I have a MacBook Pro M1 (16 GB RAM), and testing CreateML's StyleTransfer model training. When I press «Train», it starts processing and fails with error «Could not create buffer with format BGRA -6662». During the «processing» it allocates about 4.5 GB of RAM. I guess it runs out of memory, however, I've closed all other programs and I can see that there's lot of free RAM when it fails. It happens even if I use just 3 small (500*500) images, 1 for each training, content and validation. So, how to fix it?

I am trying to train an Object Detection Model using transfer learning with a small dataset ( roughly 650 Images and two classes ) using Create ML v2.0 (53.2.2) with prefer external GPU checked. I am using a 2018 Mac mini 3.2 ghz I7 16 gb of ram and AMD Radeon Pro 580 eGPU. The problem I am having is that I can only do about 3500 iterations before I run out of memory and I need to pause the training. When I resume training my Loss increases again and it takes a while for it to get back down to where it was before I paused. So I am wondering if there is a better way to setup the hardware or any other suggestions so I can get through all of the iterations without having to pause. I don't recall having this issue with Create ML v1.0, so any suggestions would be appreciated.

hello, When I used xcode to generate the model encryption key, an error was reported, the error was 'Failed to Generate Encryption Key and Sign in with you Apple ID in the Apple ID pane in System Preferences and retry '.But I have logged in my apple id in the system preferences, and this error still occurs.I reinstalled xcode and re-logged in to my apple id. This error still exists. Xcode Version 12.4 macOS Catalina 10.15.7 thanks

Hi, I'm new to Create ML. I was trying to create a simple sentiment analysis model. My input file is cleaned JSON data from Apple reviews with two fields, "label" (pos/neg) and "text" (the content with sentiment). Training runs successfully. However, when I try to perform a test with similar data with the same fields in either JSON or CSV format, I continue to get the following error. Testing Error: Expected directory at URL at "filename.json" I've tried using different data sets but always receive the same error. Does anyone have any idea what I'm doing wrong?