I’m working on real-time object detection using YOLOv8, but I only need to detect objects in approximately 40% of the screen area. Is it possible to limit the captureOut method to focus solely on that specific region of the screen? If this isn’t feasible, I’m considering an approach where the full-screen pixel buffer is captured and then cropped to the target area before running detection. However, I’m concerned about how this might affect real-time performance. I’d appreciate any insights on how to maintain real-time performance or suggestions for better alternatives. Thank you!

We are experiencing a major issue with the native .version1 of the SoundAnalysis framework in iOS 18, which has led to all our user not having recordings. Our core feature relies heavily on sound analysis in the background, and it previously worked flawlessly in prior iOS versions. However, in the new iOS 18, sound analysis stops working in the background, triggering a critical warning. Details of the issue: We are using SoundAnalysis to analyze background sounds and have enabled the necessary background permissions. We are using the latest XCode A warning now appears, and sound analysis fails in the background. Below is the warning message we are encountering: Warning Message: Execution of the command buffer was aborted due to an error during execution. Insufficient Permission (to submit GPU work from background) [Espresso::handle_ex_plan] exception=Espresso exception: "Generic error": Insufficient Permission (to submit GPU work from background) (00000006:kIOGPUCommandBufferCallbackErrorBackgroundExecutionNotPermitted); code=7 status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1). CoreML prediction failed with Error Domain=com.apple.CoreML Code=0 "Failed to evaluate model 0 in pipeline" UserInfo={NSLocalizedDescription=Failed to evaluate model 0 in pipeline, NSUnderlyingError=0x30330e910 {Error Domain=com.apple.CoreML Code=0 "Failed to evaluate model 1 in pipeline" UserInfo={NSLocalizedDescription=Failed to evaluate model 1 in pipeline, NSUnderlyingError=0x303307840 {Error Domain=com.apple.CoreML Code=0 "Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1)." UserInfo={NSLocalizedDescription=Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1).}}}}} We urgently need guidance or a fix for this, as our application’s main functionality is severely impacted by this background permission error. Please let us know the next steps or if this is a known issue with iOS 18.

Hello, I would like to inquire about the release date of Swift Assist’s beta version. Apple has stated that it will be released later this year, but they have not provided a specific date or time. Could you please provide information on the beta version’s release date? Additionally, is there a trial version available? If so, when was it released? Thank you for your assistance.

func testMLTensor() { let t1 = MLTensor(shape: [2000, 1], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 2000), scalarType: Float.self) let t2 = MLTensor(shape: [1, 3000], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 3000), scalarType: Float.self) for _ in 0...50 { let t = Date() let x = (t1 * t2) print("MLTensor", t.timeIntervalSinceNow * 1000, "ms") } } testMLTensor() The above code took more time than expected, especially in the early stage of iteration.

func testMLTensor() { let t1 = MLTensor(shape: [2000, 1], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 2000), scalarType: Float.self) let t2 = MLTensor(shape: [1, 3000], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 3000), scalarType: Float.self) for _ in 0...50 { let t = Date() let x = (t1 * t2) print("MLTensor", t.timeIntervalSinceNow * 1000, "ms") } } testMLTensor() The above code took more time than expected, especially in the early stage of iteration.

func testMLTensor() { let t1 = MLTensor(shape: [2000, 1], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 2000), scalarType: Float.self) let t2 = MLTensor(shape: [1, 3000], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 3000), scalarType: Float.self) for _ in 0...50 { let t = Date() let x = (t1 * t2) print("MLTensor", t.timeIntervalSinceNow * 1000, "ms") } } testMLTensor() The above code took more time than expected, especially in the early stage of iteration.

I'm trying to cast the error thrown by TranslationSession.translations(from:) as Translation.TranslationError. However, the app crashes at runtime whenever Translation.TranslationError is used in the project. Environment: iOS Version: 18.1 beta Xcode Version: 16 beta yld[14615]: Symbol not found: _$s11Translation0A5ErrorVMa Referenced from: <3426152D-A738-30C1-8F06-47D2C6A1B75B> /private/var/containers/Bundle/Application/043A25BC-E53E-4B28-B71A-C21F77C0D76D/TranslationAPI.app/TranslationAPI.debug.dylib Expected in: /System/Library/Frameworks/Translation.framework/Translation

I dragged a folder containing two subfolders directly into CreateML. One subfolder contains images, and the other contains labeled datasets. The number of files in the labeled dataset matches the number of image files. However, it shows "Missing data for label dianjiaoyise.jsons. Detailed list of labels missing files: ["dianjiaoyise.jsons"]."

Hello, I’m currently working on Tiny ML or ML on Edge using the Google Colab platform. Due to the exhaust of my compute unit’s free usage, I’m being prompted to pay. I’ve been considering leveraging the GPU capabilities of my iPad M1 and Intel-based Mac. Both devices utilize Thunderbolt ports capable of sharing connections up to 30GB/s. Since I’m primarily using a classification model, extensive GPU usage isn’t necessary. I’m looking for assistance or guidance on utilizing the iPad’s processor as an eGPU on my Mac, possibly through an API or Apple technology. Any help would be greatly appreciated!

We have to convert a local DOC file to PDF without any server interaction. It will be in offline mode. Any suggestion will be appreciated.

Hello everyone, I am trying to train using CreateML Version 6.0 Beta (146.1), feature extractor Image Feature Print v2. I am using 100K images for a total ~4GB on my M3 Max 48GB (MacOs 15.0 Beta (24A5279h)) The images seems to be correctly read and visualized in the Data Source section (no images with corrupted data seems to be there). When I start the training it's all fine for the first 6k ~ 7k pictures, then I receive the following error: Failed to create CVPixelBufferPool. Width = 0, Height = 0, Format = 0x00000000 It is the first time I am using it, so I don't really have so much of experience. Could you help me to understand what could be the problem? Thanks a lot

Hi everyone, I was wondering, on how accurate is the Hand Classification ML? For Example: Is it possible to understand the different letters of the Sign Language Alphabet or is it only capable of recognizing simple poses like a thumbs up?

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 am working on the neural network classifier provided on the coremltools.readme.io in the updatable->neural network section(https://coremltools.readme.io/docs/updatable-neural-network-classifier-on-mnist-dataset). I am using the same code but I get an error saying that the coremltools.converters.keras.convert does not exist. But this I know can be coreml version issue. Right know I am using coremltools version 6.2. I converted this model to mlmodel with .convert only. It got converted successfully. But I face an error in the make_updatable function saying the loss layer must be softmax output. Even the coremlt package API reference there I found its because the layer name is softmaxND but it should be softmax. Now the problem is when I convert the model from Keras sequential model to coreml model. the layer name and type change. And the softmax changes to softmaxND. Does anyone faced this issue? if I execute this builder.inspect_layers(last=4) I get this output [Id: 32], Name: sequential/dense_1/Softmax (Type: softmaxND) Updatable: False Input blobs: ['sequential/dense_1/MatMul'] Output blobs: ['Identity'] [Id: 31], Name: sequential/dense_1/MatMul (Type: batchedMatmul) Updatable: False Input blobs: ['sequential/dense/Relu'] Output blobs: ['sequential/dense_1/MatMul'] [Id: 30], Name: sequential/dense/Relu (Type: activation) Updatable: False Input blobs: ['sequential/dense/MatMul'] Output blobs: ['sequential/dense/Relu'] In the make_updatable function when I execute builder.set_categorical_cross_entropy_loss(name='lossLayer', input='Identity') I get this error ValueError: Categorical Cross Entropy loss layer input (Identity) must be a softmax layer output.