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Hi there, I'm trying to train an mlmodel using shorter file lengths. I'd like to not have a lower limit on the length of the audio. Is there any way to do this?

I'm getting an error very early in the the process and these tutorials seems very simple so I'm stumped. This tutorial seems straightforward but I can't make it past the step where I drag in image sets in. https://developer.apple.com/documentation/createml/creating_an_image_classifier_model video tutorial: https://www.youtube.com/watch?v=DSOknwpCnJ4 I have 1 folder titled "Training Data" with 2 sub-folders "img1" and "img2". When I drag my folder "Training Data" into the Training Data section I get the error: "No training data found. 0 invalid files found." I have no idea what is causing this. Images are .jpg and taken from my phone. I only have 6 total images in the initial test. I've tried it with and without an annotations.json file created in COCO Annotator, that didn't make a difference same error with or without. Big Sur 11.5.2 Create ML 3.0

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.

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?

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 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.

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?

I have trouble with importing CreateMLUI in the PlayGround. What I have tried: – Creating Mac OS blank playground – Restarting Xcode more than 5 times – Removing all the other boilerplate code – Reinstalling xCode completely – Checking PlayGround Settings platform But anyway the same: No such module “CreateMLUI” How to import this thing? Any suggestions?

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!