I'm attempting to build a Create ML Image Classification model with 60k images and 4k classes. Each time I run it it manages to extract all the image features and run over 8 iterations but then when I come back to it it has stopped with "Unexpected Error". I ran it on a MacBook Air 2020 (Intel chip) a few times and then purchased a Mac mini M1 to try it on (256GB, 8GB ram - 160 GB free space), but the same happens. Are there any logs with more details of the errors? "Unexpected Error" doesn't give me anything to go on. Thanks.

I encountered an error while experimenting with the new CreateMLComponents in playground with the following code: import CreateMLComponents import CoreML var fullyConnected = FullyConnectedNetworkRegressor<Float>.init() fullyConnected.hiddenUnitCounts = [2] let feature: AnnotatedFeature<MLShapedArray<Float>, Float> = .init(feature: .init(scalars: [2, 3], shape: [2]), annotation: 5) let fitted = try? await fullyConnected.fitted(to: [feature, feature]) print(fitted) The generated error message is included (partially) at the end of this post. I later found out that this same code works fine in an actual app. Any insights? The error message: Playground execution failed: error: Execution was interrupted, reason: EXC_BAD_INSTRUCTION (code=EXC_I386_INVOP, subcode=0x0). The process has been left at the point where it was interrupted, use "thread return -x" to return to the state before expression evaluation. * thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_INSTRUCTION (code=EXC_I386_INVOP, subcode=0x0)   * frame #0: 0x00007ffb2093728d SwiftNN`SwiftNN.Tensor.scalar<œÑ_0_0 where œÑ_0_0: SwiftNN.TensorScalar>(as: œÑ_0_0.Type) -> œÑ_0_0 + 157     frame #1: 0x00007ffb20937cbb SwiftNN`SwiftNN.Tensor.playgroundDescription.getter : Any + 91     frame #2: 0x000000010da43ac4 PlaygroundLogger`___lldb_unnamed_symbol491 + 820     frame #3: 0x000000010da45dbd PlaygroundLogger`___lldb_unnamed_symbol505 + 189 (some more lines ...) PlaygroundLogger`___lldb_unnamed_symbol428 + 124     frame #65: 0x000000010da41dad PlaygroundLogger`playground_log_hidden + 269     frame #66: 0x000000010ca59aba $__lldb_expr14`async_MainTY1_ at CreateMLComp.xcplaygroundpage:12:5     frame #67: 0x000000010ca59fb0 $__lldb_expr14`thunk for @escaping @convention(thin) @async () -> () at <compiler-generated>:0     frame #68: 0x000000010ca5a0c0 $__lldb_expr14`partial apply for thunk for @escaping @convention(thin) @async () -> () at <compiler-generated>:0

How can I add images to my assets and upload them to AnnotatedFiles in iOS app?

COCO has set a standard in Object Detection task dataset format. Is there a tool for translating this dataset format to CreateML format so that data can be used in CreateML for training and evaluation? I've found Roboflow but I would rather use a python script rather than this platform as it seems too complex for my needs.

I am trying to build a "Sing that Tune" game. For example: The app will tell the user to sing, "Row row your boat." The user will sing "Row row your boat" into the microphone. If the user's melody is close enough to the actual melody, the game is won. My question: Since I'm dealing with live audio that might be "correct" but not "exact," is the best strategy to use ShazamKit and an SHCustomCatalog, or is it better to use Create ML and sound classification? I know Create ML model can learn the difference between a baby and a firetruck, but can it learn the difference between a good guess and a wrong guess of a sung melody? Thank you, Eli

libobjc.A.dylib  objc_retain + 8  arrow_right  1 ContextKitExtraction  +[CKContextContentProviderUIScene _bestVisibleStringForView:usingExecutor:] + 1320  2 ContextKitExtraction  +[CKContextContentProviderUIScene _donateContentsOfWindow:usingExecutor:withOptions:] + 608  3 ContextKitExtraction  __78+[CKContextContentProviderUIScene extractFromScene:usingExecutor:withOptions:]_block_invoke + 72  4 ContextKitExtraction  __64-[CKContextExecutor addWorkItemToQueue:withWorkItem:andContext:]_block_invoke + 76  5 libdispatch.dylib  _dispatch_call_block_and_release + 32  6 libdispatch.dylib  _dispatch_client_callout + 20  7 libdispatch.dylib  _dispatch_main_queue_drain + 1020  8 libdispatch.dylib  _dispatch_main_queue_callback_4CF + 44  9 CoreFoundation  CFRUNLOOP_IS_SERVICING_THE_MAIN_DISPATCH_QUEUE + 16  10 CoreFoundation  __CFRunLoopRun + 2532  11 CoreFoundation  CFRunLoopRunSpecific + 600  12 GraphicsServices  GSEventRunModal + 164  13 UIKitCore  -[UIApplication _run] + 1100  14 UIKitCore  UIApplicationMain + 364  15 PDFReaderPro Free  main.m - Line 31  main + 31

Hello everyone, I am working on a simple ML project. I trained a custom model on classifying the images of US dollar bill notes. Everything seems good to me and I don't know why the classification label isn't being updated with any value. Files: https://codeshare.io/OdXzMW

Hi at all I ordered a mac pro (8-core & 580X) to use CreateML. The start of the training is flawless. As soon as I pause the training and then want to resume it, I get the message "Unable to resume training" & "Archive does not contain an DataTable". The same problem also occurred with the 16" M1 max ... I'm frustrated... what am I doing wrong? Is there a problem with CreateML? Thanks for the support in advance.

arabic fonts on ios 15 it very bad :( Many Arabs did not like it.. I hope the developers use ios 14 arabic fonts .

i am using the tabular regression method of CreateML. i see where it prints a couple of metrics such as root mean square error on the validation data, but i dont see any way to export the raw fitted numbers (e.g. training prediction), or validation numbers (e.g. validation prediction), or out of sample "testing" numbers (from the testing data set). is this possible in CreateML directly? the reason this is necessary is that you sometimes want to plot actual versus predicted and compute other metrics for regressions.

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?

Hi, is it possible to get the code for the demo app used in this presentation for the dynamic style transfer example please? thanks

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

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 have problems when viewing an App on an iPad, it distorts the image, that is, the size. The App looks good on the iPhone. Thank you for your support.