Did something change on face detection / Vision Framework on iOS 15? Using VNDetectFaceLandmarksRequest and reading the VNFaceLandmarkRegion2D to detect eyes is not working on iOS 15 as it did before. I am running the exact same code on an iOS 14 and iOS 15 device and the coordinates are different as seen on the screenshot? Any Ideas?

Hello, I posted an issue on the coremltools GitHub about my Core ML models not performing as well on iOS 17 vs iOS 16 but I'm posting it here just in case. TL;DR The same model on the same device/chip performs far slower (doesn't use the Neural Engine) on iOS 17 compared to iOS 16. Longer description The following screenshots show the performance of the same model (a PyTorch computer vision model) on an iPhone SE 3rd gen and iPhone 13 Pro (both use the A15 Bionic). iOS 16 - iPhone SE 3rd Gen (A15 Bioinc) iOS 16 uses the ANE and results in fast prediction, load and compilation times. iOS 17 - iPhone 13 Pro (A15 Bionic) iOS 17 doesn't seem to use the ANE, thus the prediction, load and compilation times are all slower. Code To Reproduce The following is my code I'm using to export my PyTorch vision model (using coremltools). I've used the same code for the past few months with sensational results on iOS 16. # Convert to Core ML using the Unified Conversion API coreml_model = ct.convert( model=traced_model, inputs=[image_input], outputs=[ct.TensorType(name="output")], classifier_config=ct.ClassifierConfig(class_names), convert_to="neuralnetwork", # compute_precision=ct.precision.FLOAT16, compute_units=ct.ComputeUnit.ALL ) System environment: Xcode version: 15.0 coremltools version: 7.0.0 OS (e.g. MacOS version or Linux type): Linux Ubuntu 20.04 (for exporting), macOS 13.6 (for testing on Xcode) Any other relevant version information (e.g. PyTorch or TensorFlow version): PyTorch 2.0 Additional context This happens across "neuralnetwork" and "mlprogram" type models, neither use the ANE on iOS 17 but both use the ANE on iOS 16 If anyone has a similar experience, I'd love to hear more. Otherwise, if I'm doing something wrong for the exporting of models for iOS 17+, please let me know. Thank you!

i'm trying to create an NLModel within a MessageFilterExtension handler. The code works fine in the main app, but when I try to use it in the extension it fails to initialize. Just this doesn't even work and gets the error below. Single line that fails. SMS_Classifier is the class xcode generated for my model. This line works fine in the main app. let mlModel = try SMS_Classifier(configuration: MLModelConfiguration()).model Error Unable to locate Asset for contextual word embedding model for local en. MLModelAsset: load failed with error Error Domain=com.apple.CoreML Code=0 "initialization of text classifier model with model data failed" UserInfo={NSLocalizedDescription=initialization of text classifier model with model data failed} Any ideas?

I'm playing with the new Vision API for iOS18, specifically with the new CalculateImageAestheticsScoresRequest API. When I try to perform the image observation request I get this error: internalError("Error Domain=NSOSStatusErrorDomain Code=-1 \"Failed to create espresso context.\" UserInfo={NSLocalizedDescription=Failed to create espresso context.}") The code is pretty straightforward: if let image = image { let request = CalculateImageAestheticsScoresRequest() Task { do { let cgImg = image.cgImage! let observations = try await request.perform(on: cgImg) let description = observations.description let score = observations.overallScore print(description) print(score) } catch { print(error) } } } I'm running it on a M2 using the simulator. Is it a bug? What's wrong?

iOS 18 App Intents while supporting iOS 17 Hello, I have an existing app that supports iOS 17. I already have three App Intents but would like to add some of the new iOS 18 app intents like ShowInAppSearchResultsIntent. However, I am having a hard time using #available or @available to limit this ShowInAppSearchResultsIntent to iOS 18 only while still supporting iOS 17. Obviously, the ShowInAppSearchResultsIntent needs to use @AssistantIntent which is iOS 18 only, so I mark that struct as @available(iOS 18, *). That works as expected. It is when I need to add this "SearchSnippetIntent" intent to the AppShortcutsProvider, that I begin to have trouble doing. See code below: struct SnippetsShortcutsAppShortcutsProvider: AppShortcutsProvider { @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { //iOS 17+ AppShortcut(intent: SnippetsNewSnippetShortcutsAppIntent(), phrases: [ "Create a New Snippet in \(.applicationName) Studio", ], shortTitle: "New Snippet", systemImageName: "rectangle.fill.on.rectangle.angled.fill") AppShortcut(intent: SnippetsNewLanguageShortcutsAppIntent(), phrases: [ "Create a New Language in \(.applicationName) Studio", ], shortTitle: "New Language", systemImageName: "curlybraces") AppShortcut(intent: SnippetsNewTagShortcutsAppIntent(), phrases: [ "Create a New Tag in \(.applicationName) Studio", ], shortTitle: "New Tag", systemImageName: "tag.fill") //iOS 18 Only AppShortcut(intent: SearchSnippetIntent(), phrases: [ "Search \(.applicationName) Studio", "Search \(.applicationName)" ], shortTitle: "Search", systemImageName: "magnifyingglass") } let shortcutTileColor: ShortcutTileColor = .blue } The iOS 18 Only AppShortcut shows the following error but none of the options seem to work. Maybe I am going about it the wrong way. 'SearchSnippetIntent' is only available in iOS 18 or newer Add 'if #available' version check Add @available attribute to enclosing static property Add @available attribute to enclosing struct Thanks in advance for your help.

Hi, The most recent version of tensorflow-metal is only available for macosx 12.0 and python up to version 3.11. Is there any chance it could be updated with wheels for macos 15 and Python 3.12 (which is the default version supported for tensrofllow 2.17+)? I'd note that even downgrading to Python 3.11 would not be sufficient, as the wheels only work for macos 12. Thanks.

Hi I'm having a problem with DataScannerViewController, I'm using the volume barcode scanning feature in my app, prior to that I was using an AVCaptureDevice with the UltraWideAngle set. After discovering DataScannerViewController, we planned to replace the previous obsolete code with DataScannerViewController, all together it was ok, when I want to set the ultra wide angle, I don't know how to start. I tried to get the minZoomFactor and I realized that I get 0.0 I tried to set zoomFactor to 1.0 and I found that he is not valid Note: func dataScannerDidZoom(_ dataScanner: DataScannerViewController), when I try to get the minZoomFactor, set the zoomFactor in this proxy method, I find that it is valid! What should I do next, I want to use only DataScannerViewController and implement ultra wide angle Thanks a lot.

Hi, I'm working with vision framework to detect barcodes. I tested both ean13 and data matrix detection and both are working fine except for the QuadrilateralProviding values in the returned BarcodeObservation. TopLeft, topRight, bottomRight and bottomLeft coordinates are rotated 90° counter clockwise (physical bottom left of data Matrix, the corner of the "L" is returned as the topLeft point in observation). The same behaviour is happening with EAN13 Barcode. Did someone else experienced the same issue with orientation? Is it normal behaviour or should we expect a fix in next releases of the Vision Framework?

Hello all, I'm working on a project that involves listening to a person speak off of a script and I want to stop then restart the recognitionTask between sections so I don't run afoul of keeping the recognitionTask running for longer than it needs to. Also, I'd like to be able to flush the current input between sections so the input from the previous section doesn't roll over into the next one. This is based on the sample code for SFSpeechRecognizer so there's a chance I might be misunderstanding something. private func restartRecording() { let inputNode = audioEngine.inputNode audioEngine.stop() inputNode.removeTap(onBus: 0) recognitionRequest?.endAudio() recordingStarted = false recognitionTask?.cancel() do { try startRecording() } catch { print("Oopsie.") } } Here's my code. When I run it, the recognition task doesn't restart. Any ideas?

I’m trying to use a Decimal as a @Property in my AppEntity, but using the following code shows me a compiler error. I’m using Xcode 16.1. The documentation notes the following: You can use the @Parameter property wrapper with common Swift and Foundation types: Primitives such as Bool, Int, Double, String, Duration, Date, Decimal, Measurement, and URL. Collections such as Array and Set. Make sure the collection’s elements are of a type that’s compatible with IntentParameter. Everything works fine for other primitives as bools, strings and integers. How do I use the Decimal though? Code struct MyEntity: AppEntity { var id: UUID @Property(title: "Amount") var amount: Decimal // … } Compiler Error This error appears at the line of the @Property definition: Generic class 'EntityProperty' requires that 'Decimal' conform to '_IntentValue'

Hello, I've been dealing with a puzzling issue for some time now, and I’m hoping someone here might have insights or suggestions. The Problem: We’re observing an occasional crash in our app that seems to originate from the Vision framework. Frequency: It happens randomly, after many successful executions of the same code, hard to tell how long the app was working, but in some cases app could run for like a month without any issues. Devices: The issue doesn't seem device-dependent (we’ve seen it on various iPad models). OS Versions: The crashes started occurring with iOS 18.0.1 and are still present in 18.1 and 18.1.1. What I suspected: The crash logs point to a potential data race within the Vision framework. The relevant section of the code where the crash happens: guard let cgImage = image.cgImage else { throw ... } let request = VNCoreMLRequest(model: visionModel) try VNImageRequestHandler(cgImage: cgImage).perform([request]) // <- the line causing the crash Since the code is rather simple, I'm not sure what else there could be missing here. The images sent here are uniform (fixed size). Model is loaded and working, the crash occurs random after a period of time and the call worked correctly many times. Also, the model variable is not an optional. Here is the crash log: libobjc.A objc_exception_throw CoreFoundation -[NSMutableArray removeObjectsAtIndexes:] Vision -[VNWeakTypeWrapperCollection _enumerateObjectsDroppingWeakZeroedObjects:usingBlock:] Vision -[VNWeakTypeWrapperCollection addObject:droppingWeakZeroedObjects:] Vision -[VNSession initWithCachingBehavior:] Vision -[VNCoreMLTransformer initWithOptions:model:error:] Vision -[VNCoreMLRequest internalPerformRevision:inContext:error:] Vision -[VNRequest performInContext:error:] Vision -[VNRequestPerformer _performOrderedRequests:inContext:error:] Vision -[VNRequestPerformer _performRequests:onBehalfOfRequest:inContext:error:] Vision -[VNImageRequestHandler performRequests:gatheredForensics:error:] OurApp ModelWrapper.perform And I'm a bit lost at this point, I've tried everything I could image so far. I've tried to putting a symbolic breakpoint in the removeObjectsAtIndexes to check if some library (e.g. crash reporter) we use didn't do some implementation swap. There was none, and if anything did some method swizzling, I'd expect that to show in the stack trace before the original code would be called. I did peek into the previous functions and I've noticed a lock used in one of the Vision methods, so in my understanding any data race in this code shouldn't be possible at all. I've also put breakpoints in the NSLock variants, to check for swizzling/override with a category and possibly messing the locking - again, nothing was there. There is also another model that is running on a separate queue, but after seeing the line with the locking in the debugger, it doesn't seem to me like this could cause a problem, at least not in this specific spot. Is there something I'm missing here, or something I'm doing wrong? Thanks in advance for your help!

I'm trying to build llama.cpp, a popular tool for running LLMs locally on macos15.1.1 (24B91) Sonoma using cmake but am encountering errors. Here is the stack overflow post regarding the issue: https://stackoverflow.com/questions/79304015/cmake-unable-to-find-foundation-framework-on-macos-15-1-1-24b91?noredirect=1#comment139853319_79304015

I am attempting to install Tensorflow on my M1 and I seem to be unable to find the correct matching versions of jax, jaxlib and numpy to make it all work. I am in Bash, because the default shell gave me issues. I downgraded to python 3.10, because with 3.13, I could not do anything right. Current actions: bash-3.2$ python3.10 -m venv ~/venv-metal bash-3.2$ python --version Python 3.10.16 python3.10 -m venv ~/venv-metal source ~/venv-metal/bin/activate python -m pip install -U pip python -m pip install tensorflow-macos And here, I keep running tnto errors like: (venv-metal):~$ pip install tensorflow-macos tensorflow-metal ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none) ERROR: No matching distribution found for tensorflow-macos What is wrong here? How can I fix that? It seems like the system wants to use the x86 version of python ... which can't be right.

Issue type: Bug TensorFlow metal version: 1.1.1 TensorFlow version: 2.18 OS platform and distribution: MacOS 15.2 Python version: 3.11.11 GPU model and memory: Apple M2 Max GPU 38-cores Standalone code to reproduce the issue: import tensorflow as tf if __name__ == '__main__': gpus = tf.config.experimental.list_physical_devices('GPU') print(gpus) Current behavior Apple silicone GPU with tensorflow-metal==1.1.0 and python 3.11 works fine with tensorboard==2.17.0 This is normal output: /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/bin/python /Users/mspanchenko/VSCode/cryptoNN/ml/core_second_window/test_tensorflow_gpus.py [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] Process finished with exit code 0 But if I upgrade tensorflow to 2.18 I'll have error: /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/bin/python /Users/mspanchenko/VSCode/cryptoNN/ml/core_second_window/test_tensorflow_gpus.py Traceback (most recent call last): File "/Users/mspanchenko/VSCode/cryptoNN/ml/core_second_window/test_tensorflow_gpus.py", line 1, in <module> import tensorflow as tf File "/Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow/__init__.py", line 437, in <module> _ll.load_library(_plugin_dir) File "/Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: __ZN3tsl8internal10LogMessageC1EPKcii Referenced from: <D2EF42E3-3A7F-39DD-9982-FB6BCDC2853C> /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: <2814A58E-D752-317B-8040-131217E2F9AA> /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so Process finished with exit code 1

Hi everyone😊, I want to implement facial recognition into my app. I was planning to use createML's image classification, but there seams to be a lot of hassle to implement (the JSON file etc.). Are there some other easy to implement options that don't involve advanced coding. Thanks, Oliver

We are using VNRecognizeTextRequest to detect text in documents, and we have noticed that even in some very clear and well-formatted documents, there are still instances where text blocks are missed. the live text also have the same issue.

I've checked on pypi.org and it appears to only have arm64 packages, has x86 with AMD been deprecated?

Not finding a lot on the Swift Assist technology announced at WWDC 2024. Does anyone know the latest status? Also, currently I use OpenAI's macOS app and its 'Work With...' functionality to assist with Xcode development, and this is okay, certainly saves copying code back and forth, but it seems like AI should be able to do a lot more to help with Xcode app development. I guess I'm looking at what people are doing with AI in Visual Studio, Cline, Cursor and other IDEs and tools like those and feel a bit left out working in Xcode. Please let me know if there are AI tools or techniques out there you use to help with your Xcode projects. Thanks in advance!

We are building an app which can reads texts. It can read english and Japanese normal texts successfully. But in some cases, we need to read Japanese tategaki (vertically aligned texts). But in that times, the same code gives no output. So, is there any need to change any configuration to read Japanese tategaki? Or is it really possible to read Japanese tategaki using vision framework? lazy var detectTextRequest = VNRecognizeTextRequest { request, error in self.resStr="\n" self.words = [:] // Get OCR result guard let res = request.results as? [VNRecognizedTextObservation] else { return } // separate the words by space let text = res.compactMap({$0.topCandidates(1).first?.string}).joined(separator: " ") var n = 0 self.wordArr=[[]] self.xs = 1 self.ys = 1 var hs = 0.0 // To compare the heights of the words // To get the original axis (top most word's axis), only once for r in res { var word = r.topCandidates(1).first?.string self.words[word ?? ""] = [r.topLeft.x, r.topLeft.y] if(self.cartLabelType == 1){ if(word?.components(separatedBy: CharacterSet(charactersIn: "/／")).count ?? 0>2){ self.xs = r.topLeft.x self.ys = r.topLeft.y } } } } }

Has anyone been able to run Tensorflow > 2.15 with Tensorflow Metal 1.1.0 on M3? I tried several times but was not successful. Seems like development on TensorFlow Metal has paused?