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

FB:FB16079804 Hello, I've made the FastAI's Cat vs Dog model into model that distinguishes lemons from limes and it all works fine in a notebook. I am now looking to transform this model into Core ML for my iOS app using TorchScript and Apple official guidelines for coremltools. Model converts but I cannot see the Preview Tab in. Xcode. Have anyone of you tried to convert to Core ML? I guess my input types are not matching with coremltools expectations for preview but I am stuck . Here is my code. import torch import coremltools as ct from fastai.vision.all import * import json from torchvision import transforms # Load your Fastai model (replace with your actual path) learn = load_learner('lemonmodel.pkl') # Example input image (you can use any image from your dataset) input_image = PILImage.create('example.jpg') # Preprocess the image (assuming you used these transforms during training) to_tensor = transforms.ToTensor() normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) input_tensor = to_tensor(input_image) input_tensor = normalize(input_tensor) # Apply normalization # Add a batch dimension input_tensor = input_tensor.unsqueeze(0) # Ensure float32 type input_tensor = input_tensor.float() # Trace the model trace = torch.jit.trace(learn.model, input_tensor) # Define the Core ML input type (considering your model's input shape) _input = ct.ImageType( name="input_1", shape=input_tensor.shape, bias=[-0.485/0.229, -0.456/0.224, -0.406/0.225], scale=1./(255*0.226) ) # Convert the model to Core ML format mlmodel = ct.convert( trace, inputs=[_input], minimum_deployment_target=ct.target.iOS14 # Optional, set deployment target ) # Set model type as 'imageClassifier' for the Preview tab mlmodel.type = 'imageClassifier' # Correct structure for preview parameters** (assuming two classes: 'lemon' and 'lime') labels_json = { "imageClassifier": { "labels": ["lemon", "lime"], "input": { "shape": list(input_tensor.shape), # Provide the actual input shape "mean": [0.485, 0.456, 0.406], # Match normalization mean "std": [0.229, 0.224, 0.225] # Match normalization std }, "output": { "shape": [1, 2] # Output shape for your model (2 classes) } } } # Setting up the metadata with correct 'preview' params mlmodel.user_defined_metadata['com.apple.coreml.model.preview.params'] = json.dumps(labels_json) # Save the model as .mlmodel mlmodel.save("LemonClassifierGemini.mlmodel") mlmodel = ct.convert( trace, inputs=[_input], minimum_deployment_target=ct.target.iOS14 # Optional, set deployment target ) # Set model type as 'imageClassifier' for the Preview tab** mlmodel.type = 'imageClassifier' # Correct structure for preview parameters** (assuming two classes: 'lemon' and 'lime') labels_json = { "imageClassifier": { "labels": ["lemon", "lime"], "input": { "shape": list(input_tensor.shape), # Provide the actual input shape "mean": [0.485, 0.456, 0.406], # Match normalization mean "std": [0.229, 0.224, 0.225] # Match normalization std }, "output": { "shape": [1, 2] # Output shape for your model (2 classes) } } } # Setting up the metadata with correct 'preview' params** mlmodel.user_defined_metadata['com.apple.coreml.model.preview.params'] = json.dumps(labels_json) # Save the model as .mlmodel mlmodel.save("LemonClassifierGemini.mlmodel") My model is : Input batch shape: torch.Size([32, 3, 192, 192]) Labels batch shape: torch.Size([32]) Validation Loss: None, Validation Metric: None Predictions shape: torch.Size([63, 2]) Targets shape: torch.Size([63]) Code for the model : searches = 'lemon','lime' path = Path('lemon_or_not') for o in searches: dest = (path/o) dest.mkdir(exist_ok=True, parents=True) download_images(dest, urls=search_images(f'{o} photo')) time.sleep(5) resize_images(path/o, max_size=400, dest=path/o) dls = DataBlock( blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=[Resize(192, method='squish')] ).dataloaders(path, bs=32) dls.show_batch(max_n=6) learn = vision_learner(dls, resnet18, metrics=error_rate) learn.fine_tune(3) is_lemon,_,probs = learn.predict(PILImage.create('lemon.jpg')) print(f"This is a: {is_lemon}.") print(f"Probability it's a lemon: {probs[0]:.4f}") This is a: lemon. Probability it's a lemon: 1.0000 learn.export('lemonmodel.pkl') I am stuck to why it doest show the Preview Tab.

Hi Apple Developer Community, I’m exploring ways to fine-tune the SNSoundClassifier to allow users of my iOS app to personalize the model by adding custom sounds or adjusting predictions. While Apple’s WWDC session on sound classification explains how to train from scratch, I’m specifically interested in using SNSoundClassifier as the base model and building/fine-tuning on top of it. Here are a few questions I have: 1. Fine-Tuning on SNSoundClassifier: Is there a way to fine-tune this model programmatically through APIs? The manual approach using macOS, as shown in this documentation is clear, but how can it be done dynamically - within the app for users or in a cloud backend (AWS/iCloud)? Are there APIs or classes that support such on-device/cloud-based fine-tuning or incremental learning? If not directly, can the classifier’s embeddings be used to train a lightweight custom layer? Training is likely computationally intensive and drains too much on battery, doing it on cloud can be right way but need the right apis to get this done. A sample code will do good. 2. Recommended Approach for In-App Model Customization: If SNSoundClassifier doesn’t support fine-tuning, would transfer learning on models like MobileNetV2, YAMNet, OpenL3, or FastViT be more suitable? Given these models (SNSoundClassifier, MobileNetV2, YAMNet, OpenL3, FastViT), which one would be best for accuracy and performance/efficiency on iOS? I aim to maintain real-time performance without sacrificing battery life. Also it is important to see architecture retention and accuracy after conversion to CoreML model. 3. Cost-Effective Backend Setup for Training: Mac EC2 instances on AWS have a 24-hour minimum billing, which can become expensive for limited user requests. Are there better alternatives for deploying and training models on user request when s/he uploads files (training data)? 4. TensorFlow vs PyTorch: Between TensorFlow and PyTorch, which framework would you recommend for iOS Core ML integration? TensorFlow Lite offers mobile-optimized models, but I’m also curious about PyTorch’s performance when converted to Core ML. 5. Metrics: Metrics I have in mind while picking the model are these: Publisher, Accuracy, Fine-Tuning capability, Real-Time/Live use, Suitability of iPhone 16, Architectural retention after coreML conversion, Reasons for unsuitability, Recommended use case. Any insights or recommended approaches would be greatly appreciated. Thanks in advance!

Hello, I’m attempting to convert a TensorFlow model to CoreML using the coremltools package, but I’m encountering an error during the conversion process. The error traceback points to an issue within the Cast operation in the MIL (Model Intermediate Layer) when it tries to perform type inference: AttributeError: 'float' object has no attribute 'astype' Here is the relevant part of the error traceback: File ~/.pyenv/versions/3.10.12/lib/python3.10/site-packages/coremltools/converters/mil/mil/ops/defs/iOS15/elementwise_unary.py", line 896, in get_cast_value return input_var.val.astype(dtype=type_map[dtype_val]) I’ve tried converting a model from the yamnet-tensorflow2 repository, and this error occurs when CoreML tries to cast a float type during the conversion of certain operations. I’m currently using Python 3.10 and coremltools version 6.0.1, with TensorFlow 2.x. Has anyone encountered a similar issue or can offer suggestions on how to resolve this? I’ve also considered that this might be related to mismatches in the model’s data types, but I’m not sure how to proceed. Platform and package versions: coremltools 6.1 tensorflow 2.10.0 tensorflow-estimator 2.10.0 tensorflow-hub 0.16.1 tensorflow-io-gcs-filesystem 0.37.1 Python 3.10.12 pip 24.3.1 from ~/.pyenv/versions/3.10.12/lib/python3.10/site-packages/pip (python 3.10) Darwin MacBook-Pro.local 24.1.0 Darwin Kernel Version 24.1.0: Thu Oct 10 21:02:27 PDT 2024; root:xnu-11215.41.3~2/RELEASE_X86_64 x86_64 Any help or pointers would be greatly appreciated!

I am developing with Apple Vision Pro to implement object tracking functionality, but each model needs to go into Create ML for training, and the training time is very long. Are there other ways to shorten training time while obtaining reference files in the same format? Additionally, can the delay in object tracking be further optimized? Although the refresh rate has been optimized, there is still a noticeable delay.

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.

Hello All, I'm developing a machine learning model for image classification, which requires managing an exceptionally large dataset comprising over 18,000 classes. I've encountered several hurdles while using Create ML, and I would appreciate any insights or advice from those who have faced similar challenges. Current Issues: Create ML Failures with Large Datasets: When using Create ML, the process often fails with errors such as "Failed to create CVPixelBufferPool." This issue appears when handling particularly large volumes of data. Custom Implementation Struggles: To bypass some of the limitations of Create ML, I've developed a custom solution leveraging the MLImageClassifier within the CreateML framework in my own SwiftUI MacOS app. Initially I had similar errors as I did in Create ML, but I discovered I could move beyond the "extracting features" stage without crashing by employing a workaround: using a timer to cancel and restart the job every 30 seconds. This method is the only way I've been able to finish the extraction phase, even with large datasets, but it causes many errors in the console if I allow it to run too long. Lack of Progress Reporting: Using MLJob<MLImageClassifier>, I've noticed that progress reporting stalls after the feature extraction phase. Although system resources indicate activity, there is no programmatic feedback on what is occurring. Things I've Tried: Data Validation: Ensured that all images in the dataset are valid and non-corrupted, which helps prevent unnecessary issues from faulty data. Custom Implementation with CreateML Framework: Developed a custom solution using the MLImageClassifier within the CreateML framework to gain more control over the training process. Timer-Based Workaround: Employed a workaround using a timer to cancel and restart the job every 30 seconds to move past the "extracting features" phase, allowing progress even with larger datasets. Monitoring System Resources: Observed ongoing system resource usage when process feedback stalled, confirming background processing activity despite the lack of progress reporting. Subset Testing: Successfully created and tested a model on a subset of the data, which validated the approach worked for smaller datasets and could produce a functioning model. Router Model Concept: Considered training multiple models for different subsets of data and implementing a "router" model to decide which specialized model to utilize based on input characteristics. What I Need Help With: Handling Large Datasets: I'm seeking strategies or best practices for effectively utilizing Create ML with large datasets. Any guidance on memory management or alternative methodologies would be immensely helpful. Improving Progress Reporting: I'm looking for ways to obtain more consistent and programmatic progress updates during the training and testing phases. I'm working on a Mac M1 Pro w/ 32GB RAM, with Apple Silicon and am fully integrated within the Apple ecosystem. I am very grateful for any advice or experiences you could share to help overcome these challenges. Thank you! I've pasted the relevant code below: func go() { if self.trainingSession == nil { self.trainingSession = createTrainingSession() } if self.startTime == nil { self.startTime = Date() } job = try! MLImageClassifier.resume(self.trainingSession) job.phase .receive(on: RunLoop.main) .sink { phase in self.phase = phase } .store(in: &cancellables) job.checkpoints .receive(on: RunLoop.main) .sink { checkpoint in self.state = "\(checkpoint)\n\(self.job.progress)" self.progress = self.job.progress.fractionCompleted + 0.2 self.updateTimeEstimates() } .store(in: &cancellables) job.result .receive(on: DispatchQueue.main) .sink(receiveCompletion: { completion in switch completion { case .failure(let error): print("Training Failed: \(error.localizedDescription)") case .finished: print("🎉🎉🎉🎉 TRAINING SESSION FINISHED!!!!") self.trainingFinished = true } }, receiveValue: { classifier in Task { await self.saveModel(classifier) } }) .store(in: &cancellables) } private func createTrainingSession() -> MLTrainingSession<MLImageClassifier> { do { print("Initializing training Data...") let trainingData: MLImageClassifier.DataSource = .labeledDirectories(at: trainingDataURL) let modelParameters = MLImageClassifier.ModelParameters( validation: .split(strategy: .automatic), augmentation: self.augmentations, algorithm: .transferLearning( featureExtractor: .scenePrint(revision: 2), classifier: .logisticRegressor ) ) let sessionParameters = MLTrainingSessionParameters( sessionDirectory: self.sessionDirectoryURL, reportInterval: 1, checkpointInterval: 100, iterations: self.numberOfIterations ) print("Initializing training session...") let trainingSession: MLTrainingSession<MLImageClassifier> if FileManager.default.fileExists(atPath: self.sessionDirectoryURL.path) && isSessionCreated(atPath: self.sessionDirectoryURL.path()) { do { trainingSession = try MLImageClassifier.restoreTrainingSession(sessionParameters: sessionParameters) } catch { print("error resuming, exiting.... \(error.localizedDescription)") fatalError() } } else { trainingSession = try MLImageClassifier.makeTrainingSession( trainingData: trainingData, parameters: modelParameters, sessionParameters: sessionParameters ) } return trainingSession } catch { print("Failed to initialize training session: \(error.localizedDescription)") fatalError() } }

In the WWDC24 What’s New In Create ML at 6:03 the presenter introduced TimeSeriesClassifier as a new component of Create ML Components. Where are documentation and code examples for this feature? My app captures accelerometer time series data that I want to classify. Thank you so much!

When I wanted to call the Reality Composer Pro scene containing Object Tracking, I tried the following code: RealityView { content in if let model = try? await Entity(named: "Scene", in: realityKitContentBundle) { content.add(model) } } Obviously, this is wrong. We need to add some configurations that can enable Object Tracking to Reality View. What do we need to add? Note:I have seen https://developer.apple.com/videos/play/wwdc2024/10101/, but I don't know much about it.