I created a model that classifies certain objects using yolov8. I noticed that the model is not working properly in my application. While the model works fine in Xcode preview, in the application it either returns the same result with 99% accuracy for each classification or does not provide any result. In Preview it looks like this: Predictions: extension CameraVC : AVCapturePhotoCaptureDelegate { func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: (any Error)?) { guard let data = photo.fileDataRepresentation() else { return } guard let image = UIImage(data: data) else { return } guard let cgImage = image.cgImage else { fatalError("Unable to create CIImage") } let handler = VNImageRequestHandler(cgImage: cgImage,orientation: CGImagePropertyOrientation(image.imageOrientation)) DispatchQueue.global(qos: .userInitiated).async { do { try handler.perform([self.viewModel.detectionRequest]) } catch { fatalError("Failed to perform detection: \(error)") } } lazy var detectionRequest: VNCoreMLRequest = { do { let model = try VNCoreMLModel(for: bestv720().model) let request = VNCoreMLRequest(model: model) { [weak self] request, error in self?.processDetections(for: request, error: error) } request.imageCropAndScaleOption = .centerCrop return request } catch { fatalError("Failed to load Vision ML model: \(error)") } }() This is where i print recognized objects: func processDetections(for request: VNRequest, error: Error?) { DispatchQueue.main.async { guard let results = request.results as? [VNRecognizedObjectObservation] else { return } var label = "" var all_results = [] var all_confidence = [] var true_results = [] var true_confidence = [] for result in results { for i in 0...results.count{ all_results.append(result.labels[i].identifier) all_confidence.append(result.labels[i].confidence) for confidence in all_confidence { if confidence as! Float > 0.7 { true_results.append(result.labels[i].identifier) true_confidence.append(confidence) } } } label = result.labels[0].identifier } print("True Results " , true_results) print("True Confidence ", true_confidence) self.output?.updateView(label:label) } } I converted the model like this: from ultralytics import YOLO model = YOLO(model_path) model.export(format='coreml', nms=True, imgsz=[720,1280])