I dragged a folder containing two subfolders directly into CreateML. One subfolder contains images, and the other contains labeled datasets. The number of files in the labeled dataset matches the number of image files. However, it shows "Missing data for label dianjiaoyise.jsons. Detailed list of labels missing files: ["dianjiaoyise.jsons"]."

I can successfully train an ActionClassifier using CreateML. However, I get crashes when I attempt to do the same asynchronously. The model parameters and training data sources are the same in both cases: let modelParameters = MLActionClassifier.ModelParameters(validation: validationDataSet,batchSize: 5, maximumIterations: 10, predictionWindowSize: 120, targetFrameRate: 30) let trainingDataSource = MLActionClassifier.DataSource.directoryWithVideosAndAnnotation(at: myStudyParticipantURLFinal, annotationFile: documentURLFinal, videoColumn: "file", labelColumn: "category", startTimeColumn: "startTime", endTimeColumn: "endTime") the only thing I add to attempt asyncrounous training is sessionParameters: let sessionDirectory = URL(fileURLWithPath: "(NSHomeDirectory())/test") // Session parameters can be provided to `train` method. let sessionParameters = MLTrainingSessionParameters( sessionDirectory: sessionDirectory, reportInterval: 10, checkpointInterval: 100, iterations: 10 ) To the final method: let trainJob = try MLActionClassifier.train(trainingData: trainingDataSource, parameters: modelParameters, sessionParameters: sessionParameters) The job crashes saying it cannot find plist files. I notice that only one plist file is written: meta.plist It seems there should also be a parameters.plist written, but it is not there.

getting this error again and again even if I tried reinstalling. Traceback (most recent call last): File "", line 1, in File "/Users/aman/LLM/env/lib/python3.8/site-packages/tensorflow/init.py", line 439, in _ll.load_library(_plugin_dir) File "/Users/aman/LLM/env/lib/python3.8/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/aman/LLM/env/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: OBJC_CLASS$_MPSGraphRandomOpDescriptor Referenced from: /Users/aman/LLM/env/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: /System/Library/Frameworks/MetalPerformanceShadersGraph.framework/Versions/A/MetalPerformanceShadersGraph

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 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

Hi, I'm working on training a createML object detector model; I've run into an issue that has me stumped - when I reach somewhere between 100,000 and 150,000 iterations my model will stop training and error out. More Details: CreateML gives me the error prompt that says it is unable to train the model please delete the model source and start from the beginning or duplicate the model and start from the beginning (slightly paraphrased) I see the following error in the createML console (my user name and UUIDs have been redacted) Unable to load model from file:///Users/<my user name>/Library/Caches/com.apple.dt.createml/projects/<UUID HERE>/sessions/checkpoint.sessions/<UUID Here>//training-000132500.checkpoint: Cannot open file:///Users/<my user name>/Library/Caches/com.apple.dt.createml/projects/<UUID Here>/sessions/checkpoint.sessions/<uuid here> //training-000132500.checkpoint/dir_archive.ini for read. Cannot open /Users/<my username>/Library/Caches/com.apple.dt.createml/projects/<UUID>/sessions/checkpoint.sessions/<UUID>//training-000132500.checkpoint/dir_archive.ini for reading I've gone into my Caches in my Library directory and I see each piece of the file path in finder UNTIL the //training-00132500 piece of the path, so I can at least confirm that createML appears to be unable to create or open the file it needs for this training session. Technology Used: Xcode 16 Apple M1 Pro MacOS 14.6.1 (23G93) I've also verified that Xcode and terminal have full disk permissions in my system preferences - I didn't see an option to add CreateML to this list. I've also ensured that my createML project and its data sources are not in iCloud and are indeed local on my desktop. Lastly, I made more space on my machine, so I should have a little over 1 TB of space. Has anybody experienced this before? Any advice? I am majorly blocked on this issue, so I hope somebody else can help shed some light on this issue! Thanks!

Hi, I'm training a model that should detect a forehand and a backend stroke. The data looks like this: activity,timestamp,Acceleration_X,Acceleration_Y,Acceleration_Z,Rotation_X,Rotation_Y,Rotation_Z forehand,0.0,0.08,-0.08,0.03,0.18,0.26,0.32 I can load it in Create ML but it's showing the acceleration and rotation x,y,z as seperate Doubles and not as one feature. What do I have to change to make this work? Thank you

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

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.

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.

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!