The Translation API introduced at Session 10117 is impressive, but limiting it to SwiftUI is restrictive. This API works great in the demo, but for more complex apps, it lacks flexibility because it is bound to SwiftUI Views. Please consider making it available in non-SwiftUI environments.

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!

Hi, i just wanna ask, Is it possible to run YOLOv3 on visionOS using the main camera to detect objects and show bounding boxes with labels in real-time? I’m wondering if camera access and custom models work for this, or if there’s a better way. Any tips?

Following WWDC24 video "Discover Swift enhancements in the Vision framework" recommendations (cfr video at 10'41"), I used the following code to perform multiple new iOS 18 `RecognizedTextRequest' in parallel. Problem: if more than 2 request are run in parallel, the request will hang, leaving the app in a state where no more requests can be started. -> deadlock I tried other ways to run the requests, but no matter the method employed, or what device I use: no more than 2 requests can ever be run in parallel. func triggerDeadlock() {} try await withThrowingTaskGroup(of: Void.self) { group in // See: WWDC 2024 Discover Siwft enhancements in the Vision framework at 10:41 // ############## THIS IS KEY let maxOCRTasks = 5 // On a real-device, if more than 2 RecognizeTextRequest are launched in parallel using tasks, the request hangs // ############## THIS IS KEY for idx in 0..<maxOCRTasks { let url = ... // URL to some image group.addTask { // Perform OCR let _ = await performOCRRequest(on: url: url) } } var nextIndex = maxOCRTasks for try await _ in group { // Wait for the result of the next child task that finished if nextIndex < pageCount { group.addTask { let url = ... // URL to some image // Perform OCR let _ = await performOCRRequest(on: url: url) } nextIndex += 1 } } } } // MARK: - ASYNC/AWAIT version with iOS 18 @available(iOS 18, *) func performOCRRequest(on url: URL) async throws -> [RecognizedText] { // Create request var request = RecognizeTextRequest() // Single request: no need for ImageRequestHandler // Configure request request.recognitionLevel = .accurate request.automaticallyDetectsLanguage = true request.usesLanguageCorrection = true request.minimumTextHeightFraction = 0.016 // Perform request let textObservations: [RecognizedTextObservation] = try await request.perform(on: url) // Convert [RecognizedTextObservation] to [RecognizedText] return textObservations.compactMap { observation in observation.topCandidates(1).first } } I also found this Swift forums post mentioning something very similar. I also opened a feedback: FB17240843

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?

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!

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.

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?

Hello, I am developing an app for the Swift Student challenge; however, I keep encountering an error when using ClassifyImageRequest from the Vision framework in Xcode: VTEST: error: perform(_:): inside 'for await result in resultStream' error: internalError("Error Domain=NSOSStatusErrorDomain Code=-1 \"Failed to create espresso context.\" UserInfo={NSLocalizedDescription=Failed to create espresso context.}") It works perfectly when testing it on a physical device, and I saw on another thread that ClassifyImageRequest doesn't work on simulators. Will this cause problems with my submission to the challenge? Thanks

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.

Is it just me or is early access image playground not available, been waiting for a little over 24hrs and still no access. (no rush for the team if there’s smth wrong) they might be busy rolling out the first few apple intelligence features (ios 18.1) public release.

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?

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.

Hi, DataScannerViewController does't recognize currencies less than 1.00 (e.g. 0.59 USD, 0.99 EUR, etc.). Why? How to solve the problem? This feature is not described in Apple documentation, is there a solution? This is my code: func makeUIViewController(context: Context) -&gt; DataScannerViewController { let dataScanner = DataScannerViewController(recognizedDataTypes: [ .text(textContentType: .currency)]) return dataScanner }

Using Tensorflow for Silicon gives inaccurate results when compared to Google Colab GPU (9-15% differences). Here are my install versions for 4 anaconda env's. I understand the Floating point precision can be an issue, batch size, activation functions but how do you rectify this issue for the past 3 years? 1.) Version TF: 2.12.0, Python 3.10.13, tensorflow-deps: 2.9.0, tensorflow-metal: 1.2.0, h5py: 3.6.0, keras: 2.12.0 2.) Version TF: 2.19.0, Python 3.11.0, tensorflow-metal: 1.2.0, h5py: 3.13.0, keras: 3.9.2, jax: 0.6.0, jax-metal: 0.1.1,jaxlib: 0.6.0, ml_dtypes: 0.5.1 3.) python: 3.10.13,tensorflow: 2.19.0,tensorflow-metal: 1.2.0, h5py: 3.13.0, keras: 3.9.2, ml_dtypes: 0.5.1 4.) Version TF: 2.16.2, tensorflow-deps:2.9.0,Python: 3.10.16, tensorflow-macos 2.16.2, tensorflow-metal: 1.2.0, h5py:3.13.0, keras: 3.9.2, ml_dtypes: 0.3.2 Install of Each ENV with common example: Create ENV: conda create --name TF_Env_V2 --no-default-packages start env: source TF_Env_Name ENV_1.) conda install -c apple tensorflow-deps , conda install tensorflow,pip install tensorflow-metal,conda install ipykernel ENV_2.) conda install pip python==3.11, pip install tensorflow,pip install tensorflow-metal,conda install ipykernel ENV_3) conda install pip python 3.10.13,pip install tensorflow, pip install tensorflow-metal,conda install ipykernel ENV_4) conda install -c apple tensorflow-deps, pip install tensorflow-macos, pip install tensor-metal, conda install ipykernel Example used on all 4 env: import tensorflow as tf cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar.load_data() model = tf.keras.applications.ResNet50( include_top=True, weights=None, input_shape=(32, 32, 3), classes=100,) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) model.fit(x_train, y_train, epochs=5, batch_size=64)

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?

Hey everyone, I've been updating my code to take advantage of the new Vision API for text recognition in macOS 15. I'm noticing some very odd behavior though, it seems like in general the new Vision API consistently produces worse results than the old API. For reference here is how I'm setting up my request. var request = RecognizeTextRequest() request.recognitionLevel = getOCRMode() // generally accurate request.usesLanguageCorrection = !disableLanguageCorrection // generally true request.recognitionLanguages = language.split(separator: ",").map { Locale.Language(identifier: String($0)) } // generally 'en' let observations = try? await request.perform(on: image) as [RecognizedTextObservation] Then I will process the results and just get the top candidate, which as mentioned above, typically is of worse quality then the same request formed with the old API. Am I doing something wrong here?

I was installing TensorFlow metal in the environment called "arm64_tf'" in anaconda using command line "python -m pip install tensorflow-metal" in terminal and it shows : ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal I have already tried using " conda install -c anaconda libffi" but it still doesn't work is there a solution ? Thanks apologies for my bad English

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