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Any idea why this code works great on iOS 12, and not on iOS 13? DNSServiceRef dnsServiceReference; DNSServiceErrorType dnsServiceQueryError = DNSServiceQueryRecord(&dnsServiceReference, kDNSServiceFlagsForceMulticast | kDNSServiceFlagsTimeout, kDNSServiceInterfaceIndexAny, [arpAddress UTF8String], kDNSServiceType_PTR, kDNSServiceClass_IN, QueryRecordCallback, (__bridge void *)(self)); if (dnsServiceQueryError == kDNSServiceErr_NoError) { DNSServiceErrorType callbackError = DNSServiceSetDispatchQueue(dnsServiceReference, dispatch_get_main_queue()); NSAssert(callbackError == kDNSServiceErr_NoError, @"there was an error!"); }

I am trying to train a model using the create ml app to do object detection on approximately 10 different classes (ultimately). Here is what I have done so far, with corresponding results:1. train 1 class with approx. 50 images. results in excellent object detection, and a model size of approx 65 MB2. train 2 classes, one with approx. 150 images, the other with 50 images. results in very good object detection and a model size of approx. 65 MB3. train 6 classes, one with approx 50 images, the rest with 150 or more images, a total of approx. 3000 images. results in varying qualitys of object detection from very good to very poor. the model size is still approx. 65 MB.Here are my questions:1. why is the model size the same regardless of complexity, number of images, and number of classes2. is there some parameters that I can change to allow the output to be larger and more precise?3. how many images should I have for each class?4. do I have to label every instance of each class in each image?