I'm trying to create an app that uses artificial intelligence technology.
One of the models provided on this website(https://developer.apple.com/machine-learning/models/) will be used.
Are there any copyright or legal issues if I create an app using the model provided by this website and distribute it to the App Store?
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I'm trying to create an app that uses artificial intelligence technology.
One of the models provided on this website(https://developer.apple.com/machine-learning/models/) will be used.
Are there any copyright or legal issues if I create an app using the model provided by this website and distribute it to the App Store?
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
Hi, I try to create some machine learning model for each stock in S&P500 index. When creating the model(Boosted tree model) I try to make it more successfully by doing hyper parameters using GridSearchCV. It takes so long to create one model so I don't want to think of creating all stocks models. I tried to work with CreateML and swift but it looks like it takes longer to run than sklearn on python.
My question is how can I make the process faster? is there any hyper parameters on CreateML on swift (I couldn't find it at docs) and how can I run this code on my GPU? (should be much faster).
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
Hello,
I have created a Neural Network → K Nearest Neighbors Classifier with python.
# followed by k-Nearest Neighbors for classification.
import coremltools
import coremltools.proto.FeatureTypes_pb2 as ft
from coremltools.models.nearest_neighbors import KNearestNeighborsClassifierBuilder
import copy
# Take the SqueezeNet feature extractor from the Turi Create model.
base_model = coremltools.models.MLModel("SqueezeNet.mlmodel")
base_spec = base_model._spec
layers = copy.deepcopy(base_spec.neuralNetworkClassifier.layers)
# Delete the softmax and innerProduct layers. The new last layer is
# a "flatten" layer that outputs a 1000-element vector.
del layers[-1]
del layers[-1]
preprocessing = base_spec.neuralNetworkClassifier.preprocessing
# The Turi Create model is a classifier, which is treated as a special
# model type in Core ML. But we need a general-purpose neural network.
del base_spec.neuralNetworkClassifier.layers[:]
base_spec.neuralNetwork.layers.extend(layers)
# Also copy over the image preprocessing options.
base_spec.neuralNetwork.preprocessing.extend(preprocessing)
# Remove other classifier stuff.
base_spec.description.ClearField("metadata")
base_spec.description.ClearField("predictedFeatureName")
base_spec.description.ClearField("predictedProbabilitiesName")
# Remove the old classifier outputs.
del base_spec.description.output[:]
# Add a new output for the feature vector.
output = base_spec.description.output.add()
output.name = "features"
output.type.multiArrayType.shape.append(1000)
output.type.multiArrayType.dataType = ft.ArrayFeatureType.FLOAT32
# Connect the last layer to this new output.
base_spec.neuralNetwork.layers[-1].output[0] = "features"
# Create the k-NN model.
knn_builder = KNearestNeighborsClassifierBuilder(input_name="features",
output_name="label",
number_of_dimensions=1000,
default_class_label="???",
number_of_neighbors=3,
weighting_scheme="inverse_distance",
index_type="linear")
knn_spec = knn_builder.spec
knn_spec.description.input[0].shortDescription = "Input vector"
knn_spec.description.output[0].shortDescription = "Predicted label"
knn_spec.description.output[1].shortDescription = "Probabilities for each possible label"
knn_builder.set_number_of_neighbors_with_bounds(3, allowed_range=(1, 10))
# Use the same name as in the neural network models, so that we
# can use the same code for evaluating both types of model.
knn_spec.description.predictedProbabilitiesName = "labelProbability"
knn_spec.description.output[1].name = knn_spec.description.predictedProbabilitiesName
# Put it all together into a pipeline.
pipeline_spec = coremltools.proto.Model_pb2.Model()
pipeline_spec.specificationVersion = coremltools._MINIMUM_UPDATABLE_SPEC_VERSION
pipeline_spec.isUpdatable = True
pipeline_spec.description.input.extend(base_spec.description.input[:])
pipeline_spec.description.output.extend(knn_spec.description.output[:])
pipeline_spec.description.predictedFeatureName = knn_spec.description.predictedFeatureName
pipeline_spec.description.predictedProbabilitiesName = knn_spec.description.predictedProbabilitiesName
# Add inputs for training.
pipeline_spec.description.trainingInput.extend([base_spec.description.input[0]])
pipeline_spec.description.trainingInput[0].shortDescription = "Example image"
pipeline_spec.description.trainingInput.extend([knn_spec.description.trainingInput[1]])
pipeline_spec.description.trainingInput[1].shortDescription = "True label"
pipeline_spec.pipelineClassifier.pipeline.models.add().CopyFrom(base_spec)
pipeline_spec.pipelineClassifier.pipeline.models.add().CopyFrom(knn_spec)
pipeline_spec.pipelineClassifier.pipeline.names.extend(["FeatureExtractor", "kNNClassifier"])
coremltools.utils.save_spec(pipeline_spec, "../Models/FaceDetection.mlmodel")
it is from the following tutorial: https://machinethink.net/blog/coreml-training-part3/
It Works and I were am to include it into my project:
I want to train the model via the MLUpdateTask:
ar batchInputs: [MLFeatureProvider] = []
let imageconstraint = (model.model.modelDescription.inputDescriptionsByName["image"]?.imageConstraint)
let imageOptions: [MLFeatureValue.ImageOption: Any] = [
.cropAndScale: VNImageCropAndScaleOption.scaleFill.rawValue]
var featureProviders = [MLFeatureProvider]()
//URLS where images are stored
let trainingData = ImageManager.getImagesAndLabel()
for data in trainingData{
let label = data.key
for imgURL in data.value{
let featureValue = try MLFeatureValue(imageAt: imgURL, constraint: imageconstraint!, options: imageOptions)
if let pixelBuffer = featureValue.imageBufferValue{
let featureProvider = FaceDetectionTrainingInput(image: pixelBuffer, label: label)
batchInputs.append(featureProvider)}}
let trainingData = MLArrayBatchProvider(array: batchInputs)
When calling the MLUpdateTask as follows, the context.model from completionHandler is null.
Unfortunately there is no other Information available from the compiler.
do{
debugPrint(context)
try context.model.write(to: ModelManager.targetURL)
}
catch{
debugPrint("Error saving the model \(error)")
}
})
updateTask.resume()
I get the following error when I want to access the context.model: Thread 5: EXC_BAD_ACCESS (code=1, address=0x0)
Can some1 more experienced tell me how to fix this?
It seems like I am missing some parameters?
I am currently not splitting the Data when training into train and test data. only preprocessing im doing is scaling the image down to 227x227 pixels.
Thanks!
Does anyone have a ready-made script/shortcut like the one shown in the video?
Hi, just got an Apple M3 Pro to try it out on some Jax operations. I see the development is actively ongoing so maybe this error can help.
This is the environment:
Metal device set to: Apple M3 Pro
systemMemory: 18.00 GB
maxCacheSize: 6.00 GB
jax: 0.4.26
jaxlib: 0.4.23
numpy: 1.26.4
python: 3.11.8 | packaged by conda-forge | (main, Feb 16 2024, 20:49:36) [Clang 16.0.6 ]
jax.devices (1 total, 1 local): [METAL(id=0)]
process_count: 1
platform: uname_result(system='Darwin', node='MKFL96VR9YT', release='23.4.0', version='Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:54 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6030', machine='arm64')
This is a minimal example which produces an error, I think due to the fft part:
from jax import numpy as np
array = np.ones((16, 16))
np.fft.fft2(array)
This is the full traceback:
Traceback (most recent call last):
File "/Users/user/Downloads/wow.py", line 5, in <module>
np.fft.fft2(array)
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/numpy/fft.py", line 216, in fft2
return _fft_core_2d('fft2', xla_client.FftType.FFT, a, s=s, axes=axes,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/numpy/fft.py", line 210, in _fft_core_2d
return _fft_core(func_name, fft_type, a, s, axes, norm)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/numpy/fft.py", line 102, in _fft_core
transformed = lax.fft(arr, fft_type, tuple(s))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/traceback_util.py", line 179, in reraise_with_filtered_traceback
return fun(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/pjit.py", line 298, in cache_miss
outs, out_flat, out_tree, args_flat, jaxpr, attrs_tracked = _python_pjit_helper(
^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/pjit.py", line 176, in _python_pjit_helper
out_flat = pjit_p.bind(*args_flat, **params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/core.py", line 2788, in bind
return self.bind_with_trace(top_trace, args, params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/core.py", line 425, in bind_with_trace
out = trace.process_primitive(self, map(trace.full_raise, args), params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/core.py", line 913, in process_primitive
return primitive.impl(*tracers, **params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/pjit.py", line 1494, in _pjit_call_impl
return xc._xla.pjit(name, f, call_impl_cache_miss, [], [], donated_argnums, # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/pjit.py", line 1471, in call_impl_cache_miss
out_flat, compiled = _pjit_call_impl_python(
^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/pjit.py", line 1406, in _pjit_call_impl_python
lowering_parameters=mlir.LoweringParameters()).compile()
^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py", line 2369, in compile
executable = UnloadedMeshExecutable.from_hlo(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py", line 2908, in from_hlo
xla_executable, compile_options = _cached_compilation(
^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py", line 2718, in _cached_compilation
xla_executable = compiler.compile_or_get_cached(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/compiler.py", line 266, in compile_or_get_cached
return backend_compile(backend, computation, compile_options,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/profiler.py", line 335, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/opt/anaconda3/envs/jaxmetal/lib/python3.11/site-packages/jax/_src/compiler.py", line 238, in backend_compile
return backend.compile(built_c, compile_options=options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
jaxlib.xla_extension.XlaRuntimeError: UNKNOWN: <unknown>:0: error: 'func.func' op One or more function input/output data types are not supported.
<unknown>:0: note: see current operation:
"func.func"() <{arg_attrs = [{mhlo.layout_mode = "default", mhlo.sharding = "{replicated}"}], function_type = (tensor<16x16xf32>) -> tensor<16x16xcomplex<f32>>, res_attrs = [{jax.result_info = "", mhlo.layout_mode = "default"}], sym_name = "main", sym_visibility = "public"}> ({
^bb0(%arg0: tensor<16x16xf32>):
%0 = "mhlo.convert"(%arg0) : (tensor<16x16xf32>) -> tensor<16x16xcomplex<f32>>
%1 = "mhlo.fft"(%0) {fft_length = dense<16> : tensor<2xi64>, fft_type = #mhlo<fft_type FFT>} : (tensor<16x16xcomplex<f32>>) -> tensor<16x16xcomplex<f32>>
"func.return"(%1) : (tensor<16x16xcomplex<f32>>) -> ()
}) : () -> ()
<unknown>:0: error: failed to legalize operation 'func.func'
<unknown>:0: note: see current operation:
"func.func"() <{arg_attrs = [{mhlo.layout_mode = "default", mhlo.sharding = "{replicated}"}], function_type = (tensor<16x16xf32>) -> tensor<16x16xcomplex<f32>>, res_attrs = [{jax.result_info = "", mhlo.layout_mode = "default"}], sym_name = "main", sym_visibility = "public"}> ({
^bb0(%arg0: tensor<16x16xf32>):
%0 = "mhlo.convert"(%arg0) : (tensor<16x16xf32>) -> tensor<16x16xcomplex<f32>>
%1 = "mhlo.fft"(%0) {fft_length = dense<16> : tensor<2xi64>, fft_type = #mhlo<fft_type FFT>} : (tensor<16x16xcomplex<f32>>) -> tensor<16x16xcomplex<f32>>
"func.return"(%1) : (tensor<16x16xcomplex<f32>>) -> ()
}) : () -> ()
I'd be happy running more tests should you need them, I'm new to this, so not sure which just yet.
Many thanks!!
Hey, i just created and trained an MLImageClassifier via the MLImageclassifier.train() method (https://developer.apple.com/documentation/createml/mlimageclassifier/train(trainingdata:parameters:sessionparameters:))
For my Trainingdata (MLImageclassifier.DataSource) i am using my directoy structure, so i got an images folder with subfolders of person1, person2, person3 etc. which contain images of the labeled persons (https://developer.apple.com/documentation/createml/mlimageclassifier/datasource/labeleddirectories(at:))
I am saving the checkpoints and sessions in my appdirectory, so i can create an MLIMageClassifier from an exisiting MLSession and/or MLCheckpoint.
My question is: is there any way to add new labels, optimally from my directoy strucutre, to an MLImageClassifier which i create from an existing MLCheckpoint/MLSession?
So like adding a person4 and training my pretrained Classifier with only that person4.
Or is it simply not possible and i have to train from the beginning everytime i want to add a new label?
Unfortunately i cannot find anything in the API.
Thanks!
Hey,
im training an MLImageClassifier via the train()-method:
guard let job = try? MLImageClassifier.train(trainingData: trainingData, parameters: modelParameter, sessionParameters: sessionParameters) else{
debugPrint("Training failed")
return
}
Unfortunately the metrics of my MLProgress, which is created from the returning MLJob while training are empty.
Code for listening on Progress:
job.progress.publisher(for: \.fractionCompleted)
.sink{[weak job] fractionCompleted in
guard let job = job else {
debugPrint("failure in creating job")
return
}
guard let progress = MLProgress(progress: job.progress) else {
debugPrint("failure in creating progress")
return
}
print("ProgressPROGRESS: \(progress)")
print("Progress: \(fractionCompleted)")
}
.store(in: &subscriptions)
Printing the Progress ends in:
MLProgress(elapsedTime: 2.2328420877456665, phase: CreateML.MLPhase.extractingFeatures, itemCount: 32, totalItemCount: Optional(39), metrics: [:])
Got the Same result when listening to MLCheckpoints, Metrics are empty aswell:
MLCheckpoint(url: URLPATH.checkpoint, phase: CreateML.MLPhase.extractingFeatures, iteration: 32, date: 2024-04-18 11:21:18 +0000, metrics: [:])
Can some1 tell me how I can access the metrics while training?
Thanks!
Hello Developers,
We are trying to convert Pytorch models to CoreML using coremltools,
while converting we used jit.trace to create trace of model where we encountered a warning that if model has controlflow and conditions it is not advisable to use trace instead convert into TorchScript using jit.script,
However after successful conversion of model into TorchScript, Now in the next step of conversion from TorchScript to CoreML here is the error we are getting when we tried to convert to coremltools python package.
This root error is so abstract that we are not able to trace-back from where its occurring.
AssertionError: Item selection is supported only on python list/tuple objects
We trying to add this above error prompt into ChatGPT and we get something like the below response from ChatGPT. But unfortunately it's not working.
The error indicates that the Core ML converter encountered a TorchScript operation involving item selection (indexing or slicing) on an object that it doesn't recognize as a Python list or tuple. The converter supports item selection only on these Python container types. This could happen if your model uses indexing on tensors or other types not recognized as list or tuple by the Core ML tools. You may need to revise the TorchScript code to ensure it only performs item selection on supported types or adjust the way tensors are indexed.
I have a trained model to identify squats (good & bad repetitions). It seems to be working perfectly in CreateML when I preview it with some test data, although once I add it to my app the model seems to be inaccurate and the majority of the time mixes up the actions. Does anyone know if the issue is code related or is it something to do with the model itself and how it analyses live data?
Below I have added one of my functions for "Good Squats" which most of the time doesn't even get called (even with lower confidence). The majority of the time the model classes everything as a bad squat even though it is clearly not.
Could the problem be that my dataset doesn't have enough videos?
print("GoodForm")
squatDetected = true
DispatchQueue.main.asyncAfter(deadline: .now() + 1.5) {
self.squatDetected = false
}
DispatchQueue.main.async {
self.showGoodFormAlert(with: confidence)
AudioServicesPlayAlertSound(SystemSoundID(1322))
}
}
Any help would be appreciated.
Hello,
I have been following the excellent/informative "Metal for Machine Learning" from WWDC19 to learn how to do on device training (I have a specific use case for this) and it is all working really well using the MPSNNGraph.
However, I would like to call my own metal compute/render function/pipeline to transform the inference result before calculating the loss, does anyone know if this possible and what would this look like in code?
Please see my current code below, at the comment I need to call an intermediate compute/render function to transform the inference result image before passing to the MPSNNForwardLossNode.
let rgbImageNode = MPSNNImageNode(handle: nil)
let inferGraph = makeInferenceGraph()
let reshape = MPSNNReshapeNode(source: inferGraph.resultImage, resultWidth: 64, resultHeight: 64, resultFeatureChannels: 4)
//Need to call render or compute pipeline to post process in the inference result image
let rgbLoss = MPSNNForwardLossNode(source:reshape.resultImage, labels:rgbImageNode, lossDescriptor:lossDescriptor)
let initGrad = MPSNNInitialGradientNode(source:rgbLoss.resultImage)
let gradNodes = initGrad.trainingGraph(withSourceGradient:nil, nodeHandler:nil)
guard let trainGraph = MPSNNGraph(device: device, resultImage: gradNodes![0].resultImage, resultImageIsNeeded: true) else{
fatalError("Unable to get training graph.")
}
Thanks
How do I add a already made CoreML model into my playground? I tried what people recommended online -- building a test project and get the .mlmodelc file and put that in the playground along with the autogenerated class for the model. However, I keep on getting so many errors.
The errors:
Unexpected duplicate tasks
Target 'help' (project 'help') has write command with output /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Intermediates.noindex/help.build/Debug-iphonesimulator/help.build/adc7818afdf4ae03fd98cdd618954541.sb
Target 'help' (project 'help') has write command with output /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Intermediates.noindex/help.build/Debug-iphonesimulator/help.build/adc7818afdf4ae03fd98cdd618954541.sb
Unexpected duplicate tasks
Showing Recent Issues
Target 'help' (project 'help'): CoreMLModelCompile /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Products/Debug-iphonesimulator/help.app/ /Users/cpulipaka/Desktop/help.swiftpm/Resources/ZooClassifier.mlmodel
Target 'help' (project 'help'): CoreMLModelCompile /Users/cpulipaka/Library/Developer/Xcode/DerivedData/help-appuguzbduqvojfwkaxtnqkozecv/Build/Intermediates.noindex/Previews/help/Products/Debug-iphonesimulator/help.app/ /Users/cpulipaka/Desktop/help.swiftpm/Resources/ZooClassifier.mlmodel
ZooClassifier.mlmodel: No predominant language detected. Set COREML_CODEGEN_LANGUAGE to preferred language.
Hi, I have a an issue with jax.numpy.linalg.inv(a).
import jax.numpy.linalg as jnpl
B = jnp.identity(2)
jnpl.inv(B)
Throws the following error:
XlaRuntimeError: UNKNOWN: /var/folders/pw/wk5rfkjj6qggqp8r8zb2bw8w0000gn/T/ipykernel_34334/2572982404.py:9:0: error: failed to legalize operation 'mhlo.triangular_solve'
/var/folders/pw/wk5rfkjj6qggqp8r8zb2bw8w0000gn/T/ipykernel_34334/2572982404.py:9:0: note: called from
/var/folders/pw/wk5rfkjj6qggqp8r8zb2bw8w0000gn/T/ipykernel_34334/2572982404.py:9:0: note: see current operation: %120 = \"mhlo.triangular_solve\"(%42#4, %119) {left_side = true, lower = true, transpose_a = #mhlo<transpose NO_TRANSPOSE>, unit_diagonal = true} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
Any ideas what could be the issue or how to solve it?
Trying to learn vision apps and I was wondering if the actual .xcodeproj file was available anywhere. I understand there are snippets of code below the video but it's difficult to learn how to build an app with those files since it just focuses on the ML aspect.
https://developer.apple.com/videos/play/wwdc2021/10039/
I'm also looking for the code for this video specifically. I'm aware of the drawing code but that is a relatively simple example to understand and the CreateML stuff isn't prevalent in that.
Hi Developers,
I want to create a Vision app on Swift Playgrounds on iPad. However, Vision does not properly function on Swift Playgrounds on iPad or Xcode Playgrounds. The Vision code only works on a normal Xcode Project.
SO can I submit my Swift Student Challenge 2024 Application as a normal Xcode Project rather than Xcode Playgrounds or Swift Playgrounds File.
Thanks :)
Hello, I am a new user with an Apple MacBook Pro.
I'm experiencing difficulties running my code through the GPU.
What do I need to install on my computer to be able to use libraries for machine learning, Computer Vision, PyTorch and Tensor Flow?
I already watch lot of tutorials on this subject, but still is looks very complicated and I need mentoring for this task.
I would greatly appreciate it if I could receive a response and if someone could guide me on this matter.
I'm exploring my Vision Pro and finding it unclear whether I can even achieve things like body pose detection etc.
https://developer.apple.com/videos/play/wwdc2023/111241/
It's clear that I can apply it to self provided images, but how about to the data coming from visionOS SDKs?
All I can find is this mesh data from ARKit, https://developer.apple.com/documentation/arkit/arkit_in_visionos - am I missing something or do we not yet have good APIs for this?
Appreciate any guidance! Thanks.
After training my dataset, the training, validation, and testing sets all show 0% in detection accuracy and all my test photos show false negative. The dataset has 1032 photos and 2 classes, and I used Roboflow for the image annotation. For network, I choose full network. If there is any way to fix this?