On accessing the CoreML Model Deployment dashboard with my developer account, the page gives a bad request error saying "Your request was invalid". Also, when I try to create a Model Collection it gives an error saying "One of the fields was invalid".

Hi, I have a core ml model that when I try to print the: modelPrediction?.labelProbability which is of type String:Double and contains all the features with their corresponding probabilities, the value of type double comes with nan rest = nan right = nan up = nan Sometimes restarting makes it work again. Sometimes it can take a lot of restarts to start working again. Even when deleting the app and installing again the same thing happens. Also tried changing the deployment version but didn't seem to fix it. Any help is appreciated.

We've 10 CoreML models in our app, each encrypted with a separate key generated in XCode. After opening and closing the app 6-7 times, the app crashes at model initialization with error: 2021-04-21 13:52:47.711729+0300 MyApp[95443:7341643] Fatal error: 'try!' expression unexpectedly raised an error: Error Domain=com.apple.CoreML Code=9 "Failed to generate key request for 08494FB2-B070-440F-A8A5-CBD0823A258E with error: -42905" UserInfo={NSLocalizedDescription=Failed to generate key request for 08494FB2-B070-440F-A8A5-CBD0823A258E with error: -42905}: file MyApp/Model.swift, line 43 Looks like iPhone is blocking the app for suspicious behavior and the app fails to decrypt the model. We noticed that after ~10 hours the app is unlocked, it successfully decrypts and initializes the model. Opening and closing the app many times in a short period of time is indeed unnatural, but the most important question is how to avoid blocking? Would Apple block the app if a user opens and closes it 10 times during a day? How does the number of models in the app affect probability that the app will be blocked? Thanks!

With the release of Xcode 13, a large section of my vision framework processing code became errors and cannot compile. All of these have became deprecated. This is my original code:  do {       // Perform VNDetectHumanHandPoseRequest       try handler.perform([handPoseRequest])       // Continue only when a hand was detected in the frame.       // Since we set the maximumHandCount property of the request to 1, there will be at most one observation.       guard let observation = handPoseRequest.results?.first else {         self.state = "no hand"         return       }       // Get points for thumb and index finger.       let thumbPoints = try observation.recognizedPoints(forGroupKey: .handLandmarkRegionKeyThumb)       let indexFingerPoints = try observation.recognizedPoints(forGroupKey: .handLandmarkRegionKeyIndexFinger)       let middleFingerPoints = try observation.recognizedPoints(forGroupKey: .handLandmarkRegionKeyMiddleFinger)       let ringFingerPoints = try observation.recognizedPoints(forGroupKey: .handLandmarkRegionKeyRingFinger)       let littleFingerPoints = try observation.recognizedPoints(forGroupKey: .handLandmarkRegionKeyLittleFinger)       let wristPoints = try observation.recognizedPoints(forGroupKey: .all)               // Look for tip points.       guard let thumbTipPoint = thumbPoints[.handLandmarkKeyThumbTIP],          let thumbIpPoint = thumbPoints[.handLandmarkKeyThumbIP],          let thumbMpPoint = thumbPoints[.handLandmarkKeyThumbMP],          let thumbCMCPoint = thumbPoints[.handLandmarkKeyThumbCMC] else {         self.state = "no tip"         return       }               guard let indexTipPoint = indexFingerPoints[.handLandmarkKeyIndexTIP],          let indexDipPoint = indexFingerPoints[.handLandmarkKeyIndexDIP],          let indexPipPoint = indexFingerPoints[.handLandmarkKeyIndexPIP],          let indexMcpPoint = indexFingerPoints[.handLandmarkKeyIndexMCP] else {         self.state = "no index"         return       }               guard let middleTipPoint = middleFingerPoints[.handLandmarkKeyMiddleTIP],          let middleDipPoint = middleFingerPoints[.handLandmarkKeyMiddleDIP],          let middlePipPoint = middleFingerPoints[.handLandmarkKeyMiddlePIP],          let middleMcpPoint = middleFingerPoints[.handLandmarkKeyMiddleMCP] else {         self.state = "no middle"         return       }               guard let ringTipPoint = ringFingerPoints[.handLandmarkKeyRingTIP],          let ringDipPoint = ringFingerPoints[.handLandmarkKeyRingDIP],          let ringPipPoint = ringFingerPoints[.handLandmarkKeyRingPIP],          let ringMcpPoint = ringFingerPoints[.handLandmarkKeyRingMCP] else {         self.state = "no ring"         return       }               guard let littleTipPoint = littleFingerPoints[.handLandmarkKeyLittleTIP],          let littleDipPoint = littleFingerPoints[.handLandmarkKeyLittleDIP],          let littlePipPoint = littleFingerPoints[.handLandmarkKeyLittlePIP],          let littleMcpPoint = littleFingerPoints[.handLandmarkKeyLittleMCP] else {         self.state = "no little"         return       }               guard let wristPoint = wristPoints[.handLandmarkKeyWrist] else {         self.state = "no wrist"         return       } ... } Now every line from thumbPoints onwards results in error, I have fixed the first part (not sure if it is correct or not as it cannot compile) to :         let thumbPoints = try observation.recognizedPoints(forGroupKey: VNHumanHandPoseObservation.JointsGroupName.thumb.rawValue)        let indexFingerPoints = try observation.recognizedPoints(forGroupKey: VNHumanHandPoseObservation.JointsGroupName.indexFinger.rawValue)        let middleFingerPoints = try observation.recognizedPoints(forGroupKey: VNHumanHandPoseObservation.JointsGroupName.middleFinger.rawValue)        let ringFingerPoints = try observation.recognizedPoints(forGroupKey: VNHumanHandPoseObservation.JointsGroupName.ringFinger.rawValue)        let littleFingerPoints = try observation.recognizedPoints(forGroupKey: VNHumanHandPoseObservation.JointsGroupName.littleFinger.rawValue)        let wristPoints = try observation.recognizedPoints(forGroupKey: VNHumanHandPoseObservation.JointsGroupName.littleFinger.rawValue) I tried many different things but just could not get the retrieving individual points to work. Can anyone help on fixing this?

Hi, I have a custom object detection CoreML model and I notice something strange when using the model with the Vision framework. I have tried two different approaches as to how to process an image and do inference on the CoreML model. The first one is using the CoreML "raw": initialising the model, getting the input image ready and using the model's .prediction() function to get the models output. The second one is using Vision to wrap the CoreML model in a VNCoreMLModel, creating a VNCoreMLRequest and using the VNImageRequestHandler to actually perform the model inference. The result of the VNCoreMLRequest is of type VNRecognizedObjectObservation. The issue I now face is in the difference in the output of both methods. The first method gives back the raw output of the CoreML model: confidence and coordinates. The confidence is an array with size equal to the number of classes in my model (3 in my case). The second method gives back the boundingBox, confidence and labels. However here the confidence is only the confidence for the most likely class (so size is equal to 1). But the confidence I get from the second approach is quite different from the confidence I get during the first approach. I can use either one of the approaches in my application. However, I really want to find out what is going on and understand how this difference occurred. Thanks!

Hello, I'm new using CoreML and I'm trying to do a test app with the models that already exist. I'm having next error at the moment to classifier the image: [coreml] Failed to get the home directory when checking model path. I would like to receive your help to solve this error. Thanks.

I have tried many times. When I change the file or re-create it, it shows 404 error { "code": 400, "message": "InvalidArgumentError: Unable to unzip MLArchive", "reason": "There was a problem with your request.", "detailedMessage": "InvalidArgumentError: Unable to unzip MLArchive", "requestUuid": "699afb97-8328-4a83-b186-851f797942aa" }

I am trying to train an image classification network in Keras with tensorflow-metal. The training freezes after the first 2-3 epochs if image augmentation layers are used (RandomFlip, RandomContrast, RandomBrightness) The system appears to use both GPU as well as CPU (as indicated by Activity Monitor). Also, warnings appear both in Jupyter and Terminal (see below). When the image augmentation layers are removed (i.e. we only rebuild the head and feed images from disk), CPU appears to be idle, no warnings appear, and training completes successfully. Versions: python 3.8, tensorflow-macos 2.11.0, tensorflow-metal 0.7.1 Sample code: img_augmentation = Sequential( [ layers.RandomFlip(), layers.RandomBrightness(factor=0.2), layers.RandomContrast(factor=0.2) ], name="img_augmentation", ) inputs = layers.Input(shape=(384, 384, 3)) x = img_augmentation(inputs) model = tf.keras.applications.EfficientNetV2S(include_top=False, input_tensor=x, weights='imagenet') model.trainable = False x = tf.keras.layers.GlobalAveragePooling2D(name="avg_pool")(model.output) x = tf.keras.layers.BatchNormalization()(x) top_dropout_rate = 0.2 x = tf.keras.layers.Dropout(top_dropout_rate, name="top_dropout")(x) outputs = tf.keras.layers.Dense(179, activation="softmax", name="pred")(x) newModel = Model(inputs=model.input, outputs=outputs, name="EfficientNet_DF20M_species") reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_accuracy', factor=0.9, patience=2, verbose=1, min_lr=0.000001) optimizer = tf.keras.optimizers.legacy.SGD(learning_rate=0.01, momentum=0.9) newModel.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) history = newModel.fit(x=train_ds, validation_data=val_ds, epochs=30, verbose=2, callbacks=[reduce_lr]) During training with image augmentation, Jupyter prints the following warnings while training the first epoch: WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op. WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op. WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op. WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op. WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op. WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op. WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2 cause there is no registered converter for this op. WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter cause there is no registered converter for this op. ... During training with image augmentation, Terminal keeps spamming the following warning: 2023-02-21 23:13:38.958633: I metal_plugin/src/kernels/stateless_random_op.cc:282] Note the GPU implementation does not produce the same series as CPU implementation. 2023-02-21 23:13:38.958920: I metal_plugin/src/kernels/stateless_random_op.cc:282] Note the GPU implementation does not produce the same series as CPU implementation. 2023-02-21 23:13:38.959071: I metal_plugin/src/kernels/stateless_random_op.cc:282] Note the GPU implementation does not produce the same series as CPU implementation. 2023-02-21 23:13:38.959115: I metal_plugin/src/kernels/stateless_random_op.cc:282] Note the GPU implementation does not produce the same series as CPU implementation. 2023-02-21 23:13:38.959359: I metal_plugin/src/kernels/stateless_random_op.cc:282] Note the GPU implementation does not produce the same series as CPU implementation. ... Any suggestions?

In the ml-ane-transformers repo, there is a custom LayerNorm implementation for the Neural Engine-optimized shape of (B,C,1,S). The coremltools documentation makes it sound like the layer_norm MIL op would support this natively. In fact, the following code works on CPU: B,C,S = 1,768,512 g,b = 1, 0 @mb.program(input_specs=[mb.TensorSpec(shape=(B,C,1,S)),]) def ln_prog(x): gamma = (torch.ones((C,), dtype=torch.float32) * g).tolist() beta = (torch.ones((C), dtype=torch.float32) * b).tolist() return mb.layer_norm(x=x, axes=[1], gamma=gamma, beta=beta, name="y") However it fails when run on the Neural Engine, giving results that are scaled by an incorrect value. Should this work on the Neural Engine?

Hello, I am reaching out for some assistance regarding integrating a CoreML action classifier into a SwiftUI app. Specifically, I am trying to implement this classifier to work with the live camera of the device. I have been doing some research, but unfortunately, I have not been able to find any relevant information on this topic. I was wondering if you could provide me with any examples, resources, or information that could help me achieve this integration? Any guidance you can offer would be greatly appreciated. Thank you in advance for your help and support.

Hello, I am a student and I am doing a search for my thesis on create ML and shape recognition and image processing, so for this subject I want to find the details of the steps used in create ML for this, such as the techniques used for pre-processing, and the methods of extracting characteristics, and the filters applied, ect...

Is it possible to create an updatable sound classifier model which uses Apple's built in MLSoundClassifier available via Create ML that can be trained/personalized on device using Core ML? I tried to look up in quite a few places for a long while, however, I know that when on-device training was initially announced in 2019, updatable models were only restricted to non built-in classifiers, but any additional information that may have come out after 2019 in this regard has been hard to find.

Hey, Are there any limits to the windowDuration property of the AudioFeaturePrint transformer such as the minimum value or maximum value? If we create a model with the Create ML UI App, upon selecting the AudioFeaturePrint as the feature extractor, we cannot go below 0.5 seconds for the window duration. Is the limit same if we programmatically create a model using the AudioFeaturePrint?

I have tried multiple playgrounds and consistently get the same error in any playground I create. There is a tabular data playground that does work but I see nothing I am not doing. Here is the code that fails with Error: cannot find 'MLDataTable' in scope /* code start */ import CoreML import Foundation import TabularData let jsonFile = Bundle.main.url(forResource: "sentiment_analysis", withExtension: "json")! let tempTable = try DataTable let dataTable = try MLDataTable(contentsOf: jsonFile) print(dataTable) /* code end */

In the video here, the speaker refers to MPSGraphTool, which is supposed to convert from CoreML and other formats to the new MPSGraphPackage format. Searching for MPSGraphTool on Google returns only that video, and there is no mention of it on the forums here or elsewhere. When can we expect the tool to be released? How can we find out more information about it? My use case is that the ANECompilerService that runs on the Mac / iOS devices to compile CoreML Models / Programs is extremely slow and unreliable for large models. It often crashes entirely, sitting at 100% CPU usage forever and never completing the task at hand, meaning the user is stuck in a loading state. This also applies in Xcode when running a performance test. I would really like to compile the graph once and just run it on device directly.

I am trying to convert a model I found on TensorFlow hub to CoreML so I can use it in an iOS app I'm developing. Converting the model so far has been quite simple except that I get an NotImplementedError when specifying ImageType as output. This is the code I used: model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=(256, 256, 3)), tf_hub.KerasLayer( "https://tfhub.dev/rishit-dagli/mirnet-tfjs/1" ) ]) model.build([1, 256, 256, 3]) # Batch input shape. mlmodel = ct.convert(model, convert_to="mlprogram", inputs=[ct.ImageType()], outputs=[ct.ImageType()]) If only the inputs are specified as ImageType, then no error occurs, but when I include a specification for the outputs as ImageType, I get this error: NotImplementedError: Image output 'Identity' has symbolic dimensions in its shape FYI: I'm using TensorFlow version 2.12 and CoreML 6.3 Is there any way around this? Or, am I doing this wrong? I'm quite new to machine learning and CoreML, so any helpful input is much appreciated. Thanks in advance!

Hi all, I just tried to integrate my ML model (TF to CoreML) into my Xcode project, but couldn't create a performance report. As far as I'm aware, you only need to drag your .mlmodel file into the Navigator. I took this model from TF Hub and converted it to CoreML, and it has images as inputs and MultiArray as outputs (don't know if that has any significance). Other than that, I haven't made any changes to the model itself. If anyone could point me in the right direction that would be very much appreciated! I've included a screenshot of the error here:

First of all this vision api is amazing. the OCR is very accurate. I've been looking to multiprocess using the vision API. I have about 2 million PDFs I want to OCR, and I want to run multiple threads/run parallel processing to OCR each. I tried pyobjc but it does not work so well. Any suggestions on tackling this problem?

I am trying to convert a core ML model created by turicreate into a formate that's compatible to use in android app. is there a direct way to do that? or should I create a new model and copy the current model weights over.

Hello fellow developers, I am currently developing an application involving machine learning models, specifically CoreML models, and I have encountered an intriguing issue that I am hoping to get some insights on. In my current scenario, I'm planning to create a simple application with minimal UI, possibly using PyQT or similar tools. Therefore, I'm seeking a way to utilize NeuralEngine and GPU for CoreML model inference in Python. I discovered the 'predict' API in CoreMLTools which allows for model inference, but I'm unsure if its performance is on par with that of a properly built MacOS application using Swift and Neural Engine. Can anyone provide insights into whether there's a considerable difference in inference performance between these two methods? Is the performance of CoreMLTools 'predict' API comparable to that of a full-fledged Swift MacOS application leveraging the Neural Engine? Any clarification or guidance on this matter would be greatly appreciated. Thanks!