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 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!

I'm using Numbers to build a spreadsheet that I'm exporting as a CSV. I then import this file into Create ML to train a word tagger model. Everything has been working fine for all the models I've trained so far, but now I'm coming across a use case that has been breaking the import process: commas within the training data. This is a case that none of Apple's examples show. My project takes Navajo text that has been tokenized by syllables and labels the parts-of-speech. Case that works... Raw text: Naaltsoos yídéeshtah. Tokens column: Naal,tsoos, ,yí,déesh,tah,. Labels column: NObj,NObj,Space,Verb,Verb,VStem,Punct Case that breaks... Raw text: óola, béésh łigaii, tłʼoh naadą́ą́ʼ, wáin, akʼah, dóó á,shįįh Tokens column with tokenized text (commas quoted): óo,la,",", ,béésh, ,łi,gaii,",", ,tłʼoh, ,naa,dą́ą́ʼ,",", ,wáin,",", ,a,kʼah,",", ,dóó, ,á,shįįh (Create ML reports mismatched columns) Tokens column with tokenized text (commas escaped): óo,la,\,, ,béésh, ,łi,gaii,\,, ,tłʼoh, ,naa,dą́ą́ʼ,\,, ,wáin,\,, ,a,kʼah,\,, ,dóó, ,á,shįįh (Create ML reports mismatched columns) Tokens column with tokenized text (commas escape-quoted): óo,la,\",\", ,béésh, ,łi,gaii,\",\", ,tłʼoh, ,naa,dą́ą́ʼ,\",\", ,wáin,\",\", ,a,kʼah,\",\", ,dóó, ,á,shįįh (record not detected by Create ML) Tokens column with tokenized text (commas escape-quoted): óo,la,"","", ,béésh, ,łi,gaii,"","", ,tłʼoh, ,naa,dą́ą́ʼ,"","", ,wáin,"","", ,a,kʼah,"","", ,dóó, ,á,shįįh (Create ML reports mismatched columns) Labels column: NSub,NSub,Punct,Space,NSub,Space,NSub,NSub,Punct,Space,NSub,Space,NSub,NSub,Punct,Space,NSub,Punct,Space,NSub,NSub,Punct,Space,Conj,Space,NSub,NSub Sample From Spreadsheet Solution Needed It's simple enough to escape commas within CSV files, but the format needed by Create ML essentially combines entire CSV records into single columns, so I'm ending up needing a CSV record that contains a mixture of commas to use for parsing and ones to use as character literals. That's where this gets complicated. For this particular use case (which seems like it would frequently arise when training a word tagger model), how should I properly escape a comma literal?

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!

I'm trying to generate a json for my training data, tried manually first and then tried using roboflow and I still get the same error: _annotations.createml.json file contains field "Index 0" that is not of type String. the json format provided by roboflow was [{"image":"menu1_jpg.rf.44dfacc93487d5049ed82952b44c81f7.jpg","annotations":[{"label":"100","coordinates":{"x":497,"y":431.5,"width":32,"height":10}}]}] any help would be greatly appreciated

I have reinstalled everything including command line tools but the CreateML frameworks fail to install, I need the framework so that I can train my auto-categorzation model which predicts category based on descriptions. I need that framework because I want to use reviision 4. please suggest advice on how do I proceed

Note: I posted this to the feedback assistant but haven't gotten a response for 3months =( FB13482199 I am trying to train a large image classifier. I have a training run for ~300000 images. Each image has a folder and the file names within the folders are somewhat random. 381 classes. I am on an M2 Pro, Sonoma 14.0 running CreateML Version 5.0 (121.1). I would prefer not to pursue the pytorch/HF -> coremltools route. CreateML seems to consistently crash ~25000-30000 images in during the feature extraction phase with "Unexpected Error". It does not seem to be due to an out of memory issue. I am looking for some guidance since it seems impossible to debug why this is consistently crashing. My initial assumption was that it could be due to blank/corrupt files. I do not think that is the case. I also checked if there were any special characters in the data/folders. I wasn't able to go through all, but did try some programatic regex. Don't think this is the case either. I attached the sysdiagnose results in feedback assistant after the crash happened. I did notice when going into /var/logs there was some write issue saying that Mac had written too much to disk. Note: I also tried Xcode 15.2-beta this time and the associated CoreML version. My questions: How can I fix this? How should I go about debugging CreateML errors in the future? 'Unexpected Error' - where can I go about getting the exact createml logs on my device? This is far too broad of an error statement Please let me know. As a note, I did successfully train a past model on ~100000 images. I am planning to 10-15x that if this run is successful. Please help, spent a lot of time gathering the extra data and to date have been an occasional power user of createml. Haven't heard back from Apple since December =/. I assume I'm not the only one with this problem, so looking for any instructions to hands on debug and help others. Thx!

The What’s New in Create ML session in WWDC24 went into great depth with time-series forecasting models (beginning at: 15:14) and mentioned these new models, capabilities, and tools for iOS 18. So, far, all I can find is API documentation. I don’t see any other session in WWDC24 covering these new time-series forecasting Create ML features. Is there more substance/documentation on how to use these with Create ML? Maybe I am looking in the wrong place but I am fairly new with ML. Are there any food truck / donut shop demo/sample code like in the video? It is of great interest to get ahead of the curve on this within business applications that may take advantage of this with inventory / ordering data.

I have rewatched WWDC22 a few times , but still not getting full understanding how to get .mlmodel model file type from components . Example with banana ripeness is cool , but what need to be added to actually have output of .mlmodel , is somewhere full sample code for this type of modular project ? Code is from [https://developer.apple.com/videos/play/wwdc2022/10019) import CoreImage import CreateMLComponents struct ImageRegressor { static let trainingDataURL = URL(fileURLWithPath: "~/Desktop/bananas") static let parametersURL = URL(fileURLWithPath: "~/Desktop/parameters") static func train() async throws -> some Transformer<CIImage, Float> { let estimator = ImageFeaturePrint() .appending(LinearRegressor()) // File name example: banana-5.jpg let data = try AnnotatedFiles(labeledByNamesAt: trainingDataURL, separator: "-", index: 1, type: .image) .mapFeatures(ImageReader.read) .mapAnnotations({ Float($0)! }) let (training, validation) = data.randomSplit(by: 0.8) let transformer = try await estimator.fitted(to: training, validateOn: validation) try estimator.write(transformer, to: parametersURL) return transformer } } I have tried to run it in Mac OS command line type app, Swift-UI but most what I had as output was .pkg with "pipeline.json, parameters, optimizer.json, optimizer"

I have been stuck at the “Early Access Requested” for about 48 hours. Usually they take about an hour or less to accept your request but it seems Like this one is very slow, is an issue on my end or Apple’s. Please let me know if there is a solution.

Hello! I've been trying to run tensorflow on my MBA M3. I previously had an Intel Mac and was able to run tensorflow without any problem. I've been working on a personal project in a directory I made on my previous Mac, that I was running through Jupyter notebook. Now every time I try to run the code, the kernel will die and I'm unsure what to do. I tried following tutorials, but every tutorial I've seen has made me create a new environment to access Jupyter Notebook, but not letting me access notebooks and files that have already been created. I tried to run this following command in terminal and received the subsequent error back. python -m pip install tensorflow-metal ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal I've installed miniforge, Xcode, and anaconda onto my computer already and wanted some assistance.

In the 2019 WWDC session Training Object Detection Models in Create ML a JSON file named: annotations_832_newdice_copy.json was show alongside with the images folder named: Dice Training Images Two Sets. Are these resources made available for devs ? I am looking to understand whether the 6000 annotations were needed to be done manually ? Meaning, they have annotated around 1000 images making 6 labels on each manually to achieve this source ? Video shows around 1000 images. Can someone please clarify.

I have images, and I annotated with polygon, actually simple trapezoid, so 4 points. I have been trying and trying but can't get Create ML to work. I am trying Object Detection. I am not a real programmer so really would greatly appreciate some guidance to help to get this model created. I think I made a Detectron2 model, and tried to get that converted into a mlmodel I need for xcode but had troubles there also. thank you. { "annotation": "IMG_1803.JPG", "annotations": [ { "label": "court", "coordinates": { "x": [ 187, 3710, 2780, 929 ], "y": [ 1689, 1770, 478, 508 ] } } ] },

Hey everyone, I am a beginner with developing and using Artificial Intelligence models. How do I integrate my createML image classification with swift. I already have have an ML model and I want to integrate it into a swiftUI app. If anyone could help, that would be great. Thank you, O3DP

In an App Playground Xcode project there is no Targets menu in the UI, When I try use the model, it says the model is not in scope. When I did it in a regular project it automatically generated a Swift Class and had no erorrs because it had a target but I see no place to add a target on an App playground.

Is it possible to train a model using CreateML to infer a relevance numeric score of a news article based on similar trained data, something like a sentiment score ? I created a Text Classifier that assigns a category label which works perfect but I would like a solution that calculates a numeric value, not a label.

I’m developing an activity classifier that I’d like to input using the JSON format of CoreMotion data. I am getting the error: Unable to parse /Users/DewG/Downloads/Testing/Step1/Testing.json. It does not appear to be in JSON record format. A SequenceType of dictionaries is expected I've verified that the format I am using is JSON via various JSON validators, so I am expecting I'm just holding it wrong. Is there an example of a JSON file with CoreMotion data that I can model after?

Hi folks, I'm trying to import data to train a model and getting the above error. I'm using the latest Xcode, have double checked the formatting in the annotations file, and used jpgrepair to remove any corruption from the data files. Next step is to try a different dataset, but is this a particular known error? (Or am I doing something obviously wrong?) 2019 Intel Mac, Xcode 15.4, macOS Sonoma 14.1.1 Thanks

I'm trying to use the Spatial model to perform Object Tracking on a .usdz file that I create. After loading the file, which I can view correctly in the console, I start the training. Initially, I notice that the disk usage on my PC increases. After several GB, the usage stops, but the training progress remains for hours at 0.00% with the message "About 8hr." How can I understand what the issue is? Has anyone else experienced the same problem? Thanks Diego

It has been about 5 days since I sent the request and it still hasn’t been accepted. I am quite disappointed as Apple usually accepts requests within a couple of hours. Please fix this ASAP.