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])
