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!

Hi. A17 Pro Neural Engine has 35 TOPS computational power. But many third-party benchmarks and articles suggest that it has a little more power than A16 Bionic. Some references are, Geekbench ML Core ML performance benchmark, 2023 edition How do we use the maximum power of A17 Pro Neural Engine? For example, I guess that logical devices of ANE on A17 Pro may be two, not one, so we may need to instantiate two Core ML models simultaneously for the purpose. Please let me know any technical hints.

I'm following Apple WWDC video (https://developer.apple.com/videos/play/wwdc2021/10037/) about how to create a recommendation model. But I'm getting this error when I run the project on that like of code from their tutorial. "Column keywords has element of unsupported type Dictionary<String, Double>." Here is the block of code took from the transcript of WWDC video that cause me issue: func featuresFromMealAndKeywords(meal: String, keywords: [String]) -> [String: Double] { // Capture interactions between content (the dish keywords) and context (meal) by // adding a copy of each keyword modified to include the meal. let featureNames = keywords + keywords.map { meal + ":" + $0 } // For each keyword, create an entry in a dictionary of features with a value of 1.0. return featureNames.reduce(into: [:]) { features, name in features[name] = 1.0 } } var trainingKeywords: [[String: Double]] = [] var trainingTargets: [Double] = [] for item in userPurchasedItems { // Add in the positive example. trainingKeywords.append( featuresFromMealAndKeywords(meal: item.meal, keywords: item.keywords)) trainingTargets.append(1.0) // Add in the negative example. let negativeKeywords = allKeywords.subtracting(item.keywords) trainingKeywords.append( featuresFromMealAndKeywords(meal: item.meal, keywords: Array(negativeKeywords))) trainingTargets.append(-1.0) } // Create the training data. var trainingData = DataFrame() trainingData.append(column: Column(name: "keywords" contents: trainingKeywords)) trainingData.append(column: Column(name: "target", contents: trainingTargets)) // Create the model. let model = try MLLinearRegressor(trainingData: trainingData, targetColumn: "target") Did DataFrame implementation changed since then and doesn't support Dictionary anymore? I'm at lost right now on how to reproduce their example.

I created a word tagging model in CreateML and am trying to make predictions with it using the following code: let text = "$30.00 7/1/2023" let model = TaggingModel() let input = TaggingModelInput(text: text) guard let output = try? model.prediction(input: input) else { fatalError("Unexpected runtime error.") } However, the output separates "$" and "30.00" as separate tokens as well as "7", "/", "1", "/", etc. Is there any way to make sure prices and dates get grouped together and to simply separate tokens based on whitespace? Any help is appreciated!

I am sending CVPixelBuffers to the input of the DeepLabV3 MLModel. I am of the understanding that it requires pixel color format 32ARGB or 32RGBA. Correct? Can 32BRGA be input? CVPixelBuffers support 32BRGA and OpenCV as well. Please note, I want to use the MLModel as trained. Neither 32RGBA no 32ARGB are supported for type CVPixelBuffer. 32ARGB: An unsupported runtime error occurs with the configuration as follows... func configureOutput() { videoOutput.setSampleBufferDelegate(self, queue: bufferQueue) videoOutput.alwaysDiscardsLateVideoFrames = true videoOutput.videoSettings = [String(kCVPixelBufferPixelFormatTypeKey): kCMPixelFormat_32ARGB]. 32RGBA: "Cannot find 'kCMPixelFormat_32rgba' in scope." The app process: Video captured pixelBuffers are sent to c++ code where openCV operations are done, creating up to 3 smaller Mats which are then converted back into pixel buffers in the Objective-C. These converted PixedBuffer are used in three ways. All are sent to the MLModel for image segmentation to identify people; the files may be sent to the photo library; or may simply be viewed on the screen. I need a color format that can support all these down stream operations/pipelines.

I want to use it in the code

I run a MiDaS CoreML model on the Device. It run well on VisionPro Simulator and iOS RealDevice. But crash on VisionPro device. crash mssage: /Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSCore/Utility/MPSLibrary.mm:550: failed assertion `MPSKernel MTLComputePipelineStateCache unable to load function ndArrayConvolution2DA14. Crashlog_com.moemiku.VisionMagicPhoto_2024-01-21-16-01-07.txt Crashlog_com.moemiku.VisionMagicPhoto_2024-01-21-16-00-39.txt

On tf version 2.11.0. I have tried to follow on a fairly standard NN example in order to convert to a CoreML model. However, I cannot get this to work and I'm not clear where it is going wrong. It would seem to be a fairly standard task - a toy example - and I can't see why the conversion would fail. Any help would be appreciated. I have tried the different approaches listed below, but it seems the conversion should just work. I have also tried running the same code pinned to: tensorflow==2.6.2 scikit-learn==0.19.2 pandas==1.1.1 And get a different sequence of errors. The Python code I used mostly comes form this example: https://lnwatson.co.uk/posts/intro_to_nn/ import pandas as pd import numpy as np import tensorflow as tf import torch from sklearn.model_selection import train_test_split from tensorflow import keras import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' np.bool = np.bool_ np.int = np.int_ print("tf version", tf.__version__) csv_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' col_names = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','Class'] df = pd.read_csv(csv_url, names = col_names) labels = df.pop('Class') labels = pd.get_dummies(labels) X = df.values y = labels.values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05) X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2) model = keras.Sequential() model.add(keras.layers.Dense(16, activation='relu', input_shape=(4,))) model.add(keras.layers.Dense(3, activation='softmax')) model.summary() model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(X_train, y_train, batch_size=12, epochs=200, validation_data=(X_val, y_val)) import coremltools as ct # Pass in `tf.keras.Model` to the Unified Conversion API mlmodel = ct.convert(model, convert_to="mlprogram") # mlmodel = ct.convert(model, source="tensorflow") # mlmodel = ct.convert(model, convert_to="neuralnetwork") # mlmodel = ct.convert( # model, # source="tensorflow", # inputs=[ct.TensorType(name="input")], # outputs=[ct.TensorType(name="output")], # minimum_deployment_target=ct.target.iOS14, # ) When using either of these 3: mlmodel = ct.convert(model, convert_to="mlprogram") mlmodel = ct.convert(model, source="tensorflow") mlmodel = ct.convert(model, convert_to="neuralnetwork") I get: mlmodel2 = ct.convert(model, source="tensorflow") ValueError: Const node 'sequential_5/dense_10/MatMul/ReadVariableOp' cannot have no value ERROR:root:sequential_5/dense_11/BiasAdd/ReadVariableOp:0 ERROR:root:[ 0.34652767 0.16202268 -0.3554725 ] Running TensorFlow Graph Passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 28.76 passes/s] Converting Frontend ==> MIL Ops: 8%|█████████████████ | 1/12 [00:00<00:00, 16710.37 ops/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~/Documents/CoreML Basic Models/NN_Keras_Iris.py:142 130 import coremltools as ct 131 # Pass in `tf.keras.Model` to the Unified Conversion API 132 # mlmodel = ct.convert(model, convert_to="mlprogram") 133 (...) 140 141 # ct.convert(mymodel(), source="tensorflow") --> 142 mlmodel2 = ct.convert(model, source="tensorflow") 144 mlmodel = ct.convert( 145 model, 146 source="tensorflow", (...) 153 minimum_deployment_target=ct.target.iOS14, 154 ) .... File ~/opt/anaconda3/envs/coreml_env/lib/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/ops.py:430, in Const(context, node) 427 @register_tf_op 428 def Const(context, node): 429 if node.value is None: --> 430 raise ValueError("Const node '{}' cannot have no value".format(node.name)) 431 mode = get_const_mode(node.value.val) 432 x = mb.const(val=node.value.val, mode=mode, name=node.name) ValueError: Const node 'sequential_5/dense_10/MatMul/ReadVariableOp' cannot have no value Second Approach: A different approach I tried was specifying the inout type TensorType. However, when specifying the input and outputs I get a different error. I have tried variations on this initialiser but all produce the same error. The variations revolve around adding input_shape, dtype=np.float32 mlmodel = ct.convert( model, source="tensorflow", inputs=[ct.TensorType(name="input")], outputs=[ct.TensorType(name="output")], minimum_deployment_target=ct.target.iOS14, ) t File ~/opt/anaconda3/envs/coreml_env/lib/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/load.py:106, in <listcomp>(.0) 104 logging.debug(msg.format(outputs)) 105 outputs = outputs if isinstance(outputs, list) else [outputs] --> 106 outputs = [i.split(":")[0] for i in outputs] 107 if _get_version(tf.__version__) < _StrictVersion("1.13.1"): 108 return tf.graph_util.extract_sub_graph(graph_def, outputs) AttributeError: 'TensorType' object has no attribute 'split'

Is 30x30 the maximum grid size on Create ML App? The input allows me to set any number higher than that, but on starting training, the number falls back to 30x30. Is that a limitation or a bug in the app?

Have a CoreML model that I run in my app Spatial Media Toolkit which lets you convert 2D photos to Spatial. Running the model on my 13" M1 mac gets 70ms inference. Running the exact same code on my Vision Pro takes 700ms. I'm working on adding video support but Vision Pro inference is feeling impossible due to 700ms per frame (20x realtime for for 30fps! 1 sec of video takes 20 sec!) There's a ModelConfiguration you can provide, and when I force CPU I get the same exact performance. Either it's only running on CPU, the NeuralEngine is throttled, or maybe GPU isn't allowed to help out. Disappointing but also feels like a software issue. Would be curious if anyone else has hit this/have any workarounds

I am trying to coremltools.converters.convert a traced PyTorch model and I got an error: PyTorch convert function for op 'intimplicit' not implemented I am trying to convert a RVC model from github. I traced the model with torch.jit.trace and it fails. So I traced down the problematic part to the ** layer : https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer/lib/infer_pack/modules.py#L188 import torch import coremltools as ct from infer.lib.infer_pack.modules import ** model = **(192, 5, dilation_rate=1, n_layers=16, ***_channels=256, p_dropout=0) model.remove_weight_norm() model.eval() test_x = torch.rand(1, 192, 200) test_x_mask = torch.rand(1, 1, 200) test_g = torch.rand(1, 256, 1) traced_model = torch.jit.trace(model, (test_x, test_x_mask, test_g), check_trace = True) x = ct.TensorType(name='x', shape=test_x.shape) x_mask = ct.TensorType(name='x_mask', shape=test_x_mask.shape) g = ct.TensorType(name='g', shape=test_g.shape) mlmodel = ct.converters.convert(traced_model, inputs=[x, x_mask, g]) I got an error RuntimeError: PyTorch convert function for op 'intimplicit' not implemented. How could I modify the **::forward so it does not generate an intimplicit operator ? Thanks David

In investigating a capture session crash, it's unclear what's causing occasional system pressure interruptions, except that it's happening on older iOS devices. Does Low Power Mode have a meaningful impact on whether these interruptions happen?

The CoreML model worked correctly in the “Preview” of “CreateML”. However, after it is put into the Xcode project and replaced the “MobileNetV2” , it did not classify the images correctly, it returned one image with high confidence all the time no matter what image it is . The same code works fine when executed on real device. Can someone please assist on this ?

I have an mlprogram of size 127.2MB it was created using tensorflow and then converted to CoreML. When I request a prediction the amount of memory shoots up to 2-2.5GB every time. I've tried using the optimization techniques in coremltools but nothing seems to work it still shoots up to the same 2-2.5GB of ram every time. I've attached a graph to see it doesn't seem to be a leak as the memory is then going back down.

I'm using Filemaker, with Monkey Bread Software plugin's CoreML features, to find that it can only write to .mlmodelc. Are these (.mlmodel = .mlmodelc) the same? If not, how do you generate a .mlmodelc using XCode. Please let me know, thanks.

This model run coreml result is not right, the precision is completely wrong, I posted a PhotoDepthAnythingConv.onnx model: https://github.com/MoonCodeMaster/CoremlErrorModel/tree/main/DepthAnything

I know that I can use face detect with CoreML, but I'm wandering that is there any to identify the same person between two images like Photos app.

for (int i = 0; i < 1000; i++){ double st_tmp = CFAbsoluteTimeGetCurrent(); retBuffer = [self.enhancer enhance:pixelBuffer error:&error]; double et_tmp = CFAbsoluteTimeGetCurrent(); NSLog(@"[enhance once] %f ms ", (et_tmp - st_tmp) * 1000); } When I run a CoreML model using the above code, I notice that the runtime gradually decreases at the beginning. output： [enhance once] 14.965057 ms [enhance once] 12.727022 ms [enhance once] 12.818098 ms [enhance once] 11.829972 ms [enhance once] 11.461020 ms [enhance once] 10.949016 ms [enhance once] 10.712981 ms [enhance once] 10.367990 ms [enhance once] 10.077000 ms [enhance once] 9.699941 ms [enhance once] 9.370089 ms [enhance once] 8.634090 ms [enhance once] 7.659078 ms [enhance once] 7.061005 ms [enhance once] 6.729007 ms [enhance once] 6.603003 ms [enhance once] 6.427050 ms [enhance once] 6.376028 ms [enhance once] 6.509066 ms [enhance once] 6.452084 ms [enhance once] 6.549001 ms [enhance once] 6.616950 ms [enhance once] 6.471038 ms [enhance once] 6.462932 ms [enhance once] 6.443977 ms [enhance once] 6.683946 ms [enhance once] 6.538987 ms [enhance once] 6.628990 ms ... In most deep learning inference frameworks, there is usually a warmup process, but typically, only the first inference is slower. Why does CoreML have a decreasing runtime at the beginning? Is there a way to make only the first inference time longer, while keeping the rest consistent? I use the CoreML model in the (void)display_pixels:(IJKOverlay *)overlay function.

Hi, i have been noticing some strange issues with using CoreML models in my app. I am using the Whisper.cpp implementation which has a coreML option. This speeds up the transcribing vs Metal. However every time i use it, the app size inside iphone settings -> General -> Storage increases - specifically the "documents and data" part, the bundle size stays consistent. The Size of the app seems to increase by the same size of the coreml model, and after a few reloads it can increase to over 3-4gb! I thought that maybe the coreml model (which is in the bundle) is being saved to file - but i can't see where, i have tried to use instruments and xcode plus lots of printing out of cache and temp directory etc, deleting the caches etc.. but no effect. I have downloaded the container of the iphone from xcode and inspected it, there are some files stored inthe cache but only a few kbs, and even though the value in the settings-> storage shows a few gb, the container is only a few mb. Please can someone help or give me some guidance on what to do to figure out why the documents and data is increasing? where could this folder be pointing to that is not in the xcode downloaded container?? This is the repo i am using https://github.com/ggerganov/whisper.cpp the swiftui app and objective-C app both do the same thing i am witnessing when using coreml. Thanks in advance for any help, i am totally baffled by this behaviour

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?