I was watching WWDC20_Model Deployment, but I found that there's no existing documentation backing up this session. Is model deployment dashboard still available in 2024?

I made a model using pytorch and then converted it into a mlmodel file. Next I tried and downloaded (https://developer.apple.com/documentation/vision/recognizing_objects_in_live_capture) which worked! But when I changed the model to my model that I made, the camera worked, but no predictions where shown please h elp!

I am currently working on a 2D pose estimator. I developed a PyTorch vision transformer based model with 17 joints in COCO format for the same and then converted it to CoreML using CoreML tools version 6.2. The model was trained on a custom dataset. However, upon running the converted model on iOS, I observed a significant drop in accuracy. You can see it in this video (https://youtu.be/EfGFrOZQGtU) that demonstrates the outputs of the PyTorch model (on the left) and the CoreML model (on the right). Could you please confirm if this drop in accuracy is expected and suggest any possible solutions to address this issue? Please note that all preprocessing and post-processing techniques remain consistent between the models. P.S. While converting I also got the following warning. : TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)): P.P.S. When we initialize the CoreML model on iOS 17.0, we get this error: Validation failure: Invalid Pool kernel width (13), must be [1-8] or 20. Validation failure: Invalid Pool kernel width (9), must be [1-8] or 20. Validation failure: Invalid Pool kernel width (13), must be [1-8] or 20. Validation failure: Invalid Pool kernel width (9), must be [1-8] or 20. Validation failure: Invalid Pool kernel width (13), must be [1-8] or 20. This neural network model does not have a parameter for requested key 'precisionRecallCurves'. Note: only updatable neural network models can provide parameter values and these values are only accessible in the context of an MLUpdateTask completion or progress handler.

https://developer.apple.com/videos/play/wwdc2024/10159/ This video references demo_utils but I did not see any source code attached to the video. Does anyone have access to it

I created a model that classifies certain objects using yolov8. I noticed that the model is not working properly in my application. While the model works fine in Xcode preview, in the application it either returns the same result with 99% accuracy for each classification or does not provide any result. In Preview it looks like this: Predictions: extension CameraVC : AVCapturePhotoCaptureDelegate { func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: (any Error)?) { guard let data = photo.fileDataRepresentation() else { return } guard let image = UIImage(data: data) else { return } guard let cgImage = image.cgImage else { fatalError("Unable to create CIImage") } let handler = VNImageRequestHandler(cgImage: cgImage,orientation: CGImagePropertyOrientation(image.imageOrientation)) DispatchQueue.global(qos: .userInitiated).async { do { try handler.perform([self.viewModel.detectionRequest]) } catch { fatalError("Failed to perform detection: \(error)") } } lazy var detectionRequest: VNCoreMLRequest = { do { let model = try VNCoreMLModel(for: bestv720().model) let request = VNCoreMLRequest(model: model) { [weak self] request, error in self?.processDetections(for: request, error: error) } request.imageCropAndScaleOption = .centerCrop return request } catch { fatalError("Failed to load Vision ML model: \(error)") } }() This is where i print recognized objects: func processDetections(for request: VNRequest, error: Error?) { DispatchQueue.main.async { guard let results = request.results as? [VNRecognizedObjectObservation] else { return } var label = "" var all_results = [] var all_confidence = [] var true_results = [] var true_confidence = [] for result in results { for i in 0...results.count{ all_results.append(result.labels[i].identifier) all_confidence.append(result.labels[i].confidence) for confidence in all_confidence { if confidence as! Float > 0.7 { true_results.append(result.labels[i].identifier) true_confidence.append(confidence) } } } label = result.labels[0].identifier } print("True Results " , true_results) print("True Confidence ", true_confidence) self.output?.updateView(label:label) } } I converted the model like this: from ultralytics import YOLO model = YOLO(model_path) model.export(format='coreml', nms=True, imgsz=[720,1280])

We are currently working on implementing a baby cry detection model in the frontend of our app but have encountered some challenges with the mel spectrogram transformation. Our mel spectrogram class, developed in python, leverages librosa for generating mel spectrograms (librosa.feature.melspectrogram and librosa.power_to_db). While we have successfully exported the model to a .mlmodel file, the results we obtain in Swift differ significantly from those generated by our Python code. Could this discrepancy be due to the use of librosa in Python, which might not be directly compatible with Swift? Or should the transformation process be inherently consistent once exported to a .mlmodel file?

I'm trying to convert a TensorFlow model that I didn't create and know approximately nothing about to CoreML so that I can use it in some functional tests. I can't tell you much about the model, but you can read about it on the blog from the team that created it: https://research.google/blog/improving-mobile-app-accessibility-with-icon-detection/ I can't convert this model to a TensorFlow Lite model because it uses a few full TensorFlow operations (which I could work around) and it exceeds the 4-tensor output limit (which I can't, AFAIK). So instead, I'm trying to convert the model to CoreML so that I can run it on-device. The issue I'm running into is that every approach fails in different ways. If I load the model with tf.saved_model.load and pass that as the first parameter to the convert call, it says NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <tensorflow.python.trackable.autotrackable.AutoTrackable object at 0x30d90c250> If I pass model.signatures['serving_default'] as the first parameter to convert, I get NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got ConcreteFunction [...a page or two of info about the function here...] If I try to wrap it in a Keras layer using the instructions provided in the converter, it fails because a sequential model can't have multiple outputs. If I try to use a tf.keras.layers.TFSMLayer to load the model, it fails because there are multiple tags, and there's no way to specify tags when constructing the layer. (It tells me that I need to add 'tags' to load the model, but if I do that, it tells me that tags isn't a valid parameter to the call.) If I load the model with tf.saved_model.load and specify a single tag, then re-save it in a different location with tf.saved_model.save to generate a new model with only a single tag, then do input_layer = tf.keras.Input(shape=(768, 768, 3), dtype="int8") layer = tf.keras.layers.TFSMLayer("./serve_model", call_endpoint='serving_default') outputs = layer(input_layer) model = tf.keras.Model(input_layer, outputs) I get AttributeError: 'Functional' object has no attribute '_get_save_spec' At one point, I also tried this: class LayerFromSavedModel(tf.keras.layers.Layer): def __init__(self): super(LayerFromSavedModel, self).__init__() self.vars = legacy_model.variables def call(self, inputs): return legacy_model.signatures['serving_default'](inputs) input = tf.keras.Input(shape=(3000, 3000, 3)) model = tf.keras.Model(input, LayerFromSavedModel()(input)) and saw a similar failure. I've run out of ideas here. Is there simply no support whatsoever in the converter for importing a TensorFlow 2 SavedModel into CoreML, or am I missing something fundamental?

Here is an App using CoreML API with ML package format, it works fine in iOS17, while it is crashed when calling [MLModel modelWithContentsOfURL ] to load model running in iOS18. It seems an exception is raised "Failed to set compute_device_types_mask E5RT: Cannot provide zero compute device types. (1)". Is it a bug of iOS18 beta version , and it will be fixed in the future? The stack is as below: Exception Codes: #0 at 0x1e9280254 Crashed Thread: 49 Application Specific Information: *** Terminating app due to uncaught exception 'NSGenericException', reason: 'Failed to set compute_device_types_mask E5RT: Cannot provide zero compute device types. (1)' Last Exception Backtrace: 0 CoreFoundation 0x0000000199466418 __exceptionPreprocess + 164 1 libobjc.A.dylib 0x00000001967cde88 objc_exception_throw + 76 2 CoreFoundation 0x0000000199560794 -[NSException initWithCoder:] 3 CoreML 0x00000001b4fcfa8c -[MLE5ProgramLibraryOnDeviceAOTCompilationImpl createProgramLibraryHandleWithRespecialization:error:] + 1584 4 CoreML 0x00000001b4fcf3cc -[MLE5ProgramLibrary _programLibraryHandleWithForceRespecialization:error:] + 96 5 CoreML 0x00000001b4fc23d8 __44-[MLE5ProgramLibrary prepareAndReturnError:]_block_invoke + 60 6 libdispatch.dylib 0x00000001a12e1160 _dispatch_client_callout + 20 7 libdispatch.dylib 0x00000001a12f07b8 _dispatch_lane_barrier_sync_invoke_and_complete + 56 8 CoreML 0x00000001b4fc3e98 -[MLE5ProgramLibrary prepareAndReturnError:] + 220 9 CoreML 0x00000001b4fc3bc0 -[MLE5Engine initWithContainer:configuration:error:] + 220 10 CoreML 0x00000001b4fc3888 +[MLE5Engine loadModelFromCompiledArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] + 344 11 CoreML 0x00000001b4faf53c +[MLLoader _loadModelWithClass:fromArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] + 364 12 CoreML 0x00000001b4faedd4 +[MLLoader _loadModelFromArchive:configuration:modelVersion:compilerVersion:loaderEvent:useUpdatableModelLoaders:loadingClasses:error:] + 540 13 CoreML 0x00000001b4f9b900 +[MLLoader _loadWithModelLoaderFromArchive:configuration:loaderEvent:useUpdatableModelLoaders:error:] + 424 14 CoreML 0x00000001b4faaeac +[MLLoader _loadModelFromArchive:configuration:loaderEvent:useUpdatableModelLoaders:error:] + 460 15 CoreML 0x00000001b4fb0428 +[MLLoader _loadModelFromAssetAtURL:configuration:loaderEvent:error:] + 240 16 CoreML 0x00000001b4fb00c4 +[MLLoader loadModelFromAssetAtURL:configuration:error:] + 104 17 CoreML 0x00000001b5314118 -[MLModelAssetResourceFactoryOnDiskImpl modelWithConfiguration:error:] + 116 18 CoreML 0x00000001b5418cc0 __60-[MLModelAssetResourceFactory modelWithConfiguration:error:]_block_invoke + 72 19 libdispatch.dylib 0x00000001a12e1160 _dispatch_client_callout + 20 20 libdispatch.dylib 0x00000001a12f07b8 _dispatch_lane_barrier_sync_invoke_and_complete + 56 21 CoreML 0x00000001b5418b94 -[MLModelAssetResourceFactory modelWithConfiguration:error:] + 276 22 CoreML 0x00000001b542919c -[MLModelAssetModelVendor modelWithConfiguration:error:] + 152 23 CoreML 0x00000001b5380ce4 -[MLModelAsset modelWithConfiguration:error:] + 112 24 CoreML 0x00000001b4fb0b3c +[MLModel modelWithContentsOfURL:configuration:error:] + 168

I am testing the new scaled dot product attention CoreML op on macOS 15 beta 1. Based on the session video I was expecting to see a speedup when running on GPU however I see roughly equivalent performance to the same model on macOS 14. I ran tests with two models: one that simply repeats y = sdpa(y, k, v) 50 times gpt2 124M converted from nanoGPT (the only change is not returning loss from the forward method) I converted both models using coremltools 8.0b1 with minimum deployment targets of macOS 14 and also macOS 15. In Xcode, I can see that the new op was used for the macOS 15 target. Running on macOS 15 both target models take the same time, and that time matches the runtime on macOS 14. Should I be seeing performance improvements?

The DINO v1/v2 models are particularly interesting to me as they produce embeddings for the detected objects rather than ordinary classification indexes. That makes them so much more useful than the CNN based models. I would like to prepare some of the models posted on Huggingface to run on Apple Silicon, but it seems that the default conversion with TorchScript will not work. The other default conversions I've looked at so far also don't work. Conversion based on an example input doesn't capture enough of the model. I know that some have managed to convert it as I have a demo with a coreml model that seems to work, but I would like to know how to do the conversion myself. Has anyone managed to convert any of the DINOv2 models?

Hi, I'd like to use a MLX model already in the MLX Community in my App. I understand I first need to convert it to Core ML format. Is there an easy way to do that considering MLX is an Apple project? It would be good if it was easier then I'd be more motivated to use MLX to train my models. Thanks Richard

I have a model that uses ‘flatten’, and when I converted it to a Core ML model and ran it on Xcode with an iPhone XR, I noticed that ‘flatten’ was automatically converted to ‘reshape’. However, the NPU does not support ‘reshape’. howerver, I got the Resnet50 model on apple models and performance it on XCode with the same iphone XR, I can see the 'flatten' operator which run on NPU. On the other hand, when I used the following code to convert ResNet50 in PyTorch and ran it on Xcode Performance, the ‘flatten’ operation was converted to ‘reshape’, which then ran on the CPU. ? So I dont know how to keep 'flatten' operator when convert to ml model ? coreml tool 7.1 iphone XR ios 17.5.1 from torchvision import models import coremltools as ct import torch import torch.nn as nn network_name = "my_resnet50" torch_model = models.resnet50(pretrained=True) torch_model.eval() width = 224 height = 224 example_input = torch.rand(1, 3, height, width) traced_model = torch.jit.trace(torch_model, (example_input)) model = ct.convert( traced_model, convert_to = "neuralnetwork", inputs=[ ct.TensorType( name = "data", shape = example_input.shape, dtype = np.float32 ) ], outputs = [ ct.TensorType( name = "output", dtype = np.float32 ) ], compute_units = ct.ComputeUnit.CPU_AND_NE, minimum_deployment_target = ct.target.iOS14, ) model.save("my_resnet.mlmodel") ResNet50 on Resnet50.mlmodel My Convertion of ResNet50

Error when trying to generate CoreML performance report, message says The data couldn't be written because it isn't in the correct format. Here is the code to replicate the issue import numpy as np import coremltools as ct from coremltools.converters.mil import Builder as mb import coremltools.converters.mil as mil w = np.random.normal(size=(256, 128, 1)) wemb = np.random.normal(size=(1, 32000, 128)) # .astype(np.float16) rope_emb = np.random.normal(size=(1, 2048, 128)) shapes = [(1, seqlen) for seqlen in (32, 64)] enum_shape = mil.input_types.EnumeratedShapes(shapes=shapes) fixed_shape = (1, 128) max_length = 2048 dtype = np.float32 @mb.program( input_specs=[ # mb.TensorSpec(enum_shape.symbolic_shape, dtype=mil.input_types.types.int32), mb.TensorSpec(enum_shape.symbolic_shape, dtype=mil.input_types.types.int32), ], opset_version=mil.builder.AvailableTarget.iOS17, ) def flex_like(input_ids): indices = mb.fill_like(ref_tensor=input_ids, value=np.array(1, dtype=np.int32)) causal_mask = np.expand_dims( np.triu(np.full((max_length, max_length), -np.inf, dtype=dtype), 1), axis=0, ) mask = mb.gather( x=causal_mask, indices=indices, axis=2, batch_dims=1, name="mask_gather_0", ) # mask = mb.gather( # x=mask, indices=indices, axis=1, batch_dims=1, name="mask_gather_1" # ) rope = mb.gather(x=rope_emb.astype(dtype), indices=indices, axis=1, batch_dims=1, name="rope") hidden_states = mb.gather(x=wemb.astype(dtype), indices=input_ids, axis=1, batch_dims=1, name="embedding") return ( hidden_states, mask, rope, ) cml_flex_like = ct.convert( flex_like, compute_units=ct.ComputeUnit.ALL, compute_precision=ct.precision.FLOAT32, minimum_deployment_target=ct.target.iOS17, inputs=[ ct.TensorType(name="input_ids", shape=enum_shape), ], ) cml_flex_like.save("flex_like_32") If I remove the hidden states from the return it does work, and it also works if I keep the hidden states, but remove both mask, and rope, i.e, the report is generated for both programs with either these returns: return ( # hidden_states, mask, rope, ) and return ( hidden_states, # mask, # rope, ) It also works if I use a static shape instead of an EnumeratedShape I'm using macOS 15.0 and Xcode 16.0 Edit 1: Forgot to mention that although the performance report fails, the model is still able to make predictions

For some reason YDF does not work with the ARM processor. An issue with mutex and destruction.

I use SoundAnalysis to analyze background sounds and have enabled background permissions. It worked well in previous iOS systems, but a warning appeared in the new iOS18beta version and sound analysis was stopped. Warning List: 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).}}}}}

Hi there, I am trying to create a Message Filter app that uses a trained Text Classification to predict scam texts (as it is common in my country and is constantly evolving). However, when I try to use the MLModel in the MessageFilterExtension class, I'm getting initialization of text classifier model with model data failed Here's how I initialize my MLModel that is created using Create ML. do { let model = try MyModel(configuration: .init()) let output = try model.prediction(text: text) guard !output.label.isEmpty else { return nil } return MessagePrediction(rawValue: output.label) } catch { return nil } Is it impossible to use CoreML in Message Filter extensions? Thank you

Hi all, I'm trying to build a scam detection in Message Filter powered by CoreML. I find the predictions of ML reliable and the solution for text frauds and scams are sorely needed. I was able to create a trained MLModel and deploy it in the app. It works on my container app, but when I try to use and initialise the model in the Message Filter extension, I get an error; initialization of text classifier model with model data failed I have tried putting the model in the container app, extension, even made a shared framework for container and extension but to no avail. Every time I invoke the codes to init my model from the extension, I am met with the same error. Here's my code for initializing the model do { let model = try Ace_v24_6(configuration: .init()) let output = try model.prediction(text: text) guard !output.label.isEmpty else { return nil } return MessagePrediction(rawValue: output.label) } catch { return nil } My question is: Is it impossible to use CoreML in MessageFilters? Cheers

I have a couple of models that I want to migrate to .mlpackage but can not find the resources of the session: https://developer.apple.com/videos/play/wwdc2024/10159/ In the video at 21:10 talk about modifications and optimizations, but in the video can not even see the dependencies of the demo. Thanks

Deploy machine learning and AI models on-device with Core ML say the performance report can see the ops run on which unit and why it cannot run on Neural Engine. I tested my model and the report shows a gray checkmark at the Neural Engine, indicating it can run on the Neural Engine. However, it's not executing on the Neural Engine but on the CPU. Why is this happening?

Hi, I want to create a real time sports analytics app that takes camera input and records basketball stats. I want to use pose estimation and object classification to record things such as dribbles, when the ball leaves one's hands. etc. Is it possible to have a model in CoreML that performs pose estimation on people but also does just simple object detection on other classes (ie. ball, hoop?) Thanks