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Hi, we have a fairly large Objective-C only codebase for iOS and macOS. We plan to introduce a new, isolated cross-platform view and figured to give Swift and SwiftUI a try in this scenario. When preparing the project file, we ran into a linker error when using SwiftUI previews. The error was caused in the AppCenter framework and only occurs on iOS. macOS had no issue starting the preview, even though it uses the same framework. Our solution was to add the required Libraries and Frameworks within the conditional Other Linker Flags parameter of the project settings. However, we are still curious on why the error occurs only on iOS and why SwiftUI previews have linker issues while normal simulator / device builds are fine.

I am using a VNSequenceRequestHandler to track rectangles detected by VNDetectTextRectanglesRequest s. This works flawlessly under iOS 12, however under iOS 11.4.1 the tracking fails.I see two error messages that are logged each time I attempt to track, at every frame.Error: VNRectangleObservation object is expected to initialize Rectangle Tracker Error: No objects to track passed to the trackerAs the perform method on the VNSequenceRequestHandler can throw, this is the error returned by the method:Error Domain=com.apple.vis Code=14 "No objects to track passed to the tracker" UserInfo={NSLocalizedDescription=No objects to track passed to the tracker}Since this works under iOS 12 (with no code changes), I assume there has been a bug fix or extension of the internal functionality to make this work there. Is there anything I can do to make this work under iOS 11 or is this more of an unsupported thing?

Hi,I have modified the sample OCR model on the Keras Github page (https://github.com/fchollet/keras/blob/master/examples/image_ocr.py) to detect numbers.I am able to successfuly convert the model into the CoreML format. However, some of the layers are recognized as input and output layers, but arent really ones.The model itself seems to be fine. However, the model does not recognize numbers once it is converted. I have set the image_scale to 1/255.0 as the input seems to be grayscale with values from 0 to 1. This is visible in the example beginning at line 78.I am not sure if I did anything wrong during conversion.Maybe those GRU layers cause some issues?Here is the model:____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== the_input (InputLayer) (None, 35, 35, 1) 0 ____________________________________________________________________________________________________ conv1 (Conv2D) (None, 35, 35, 16) 160 the_input[0][0] ____________________________________________________________________________________________________ max1 (MaxPooling2D) (None, 17, 17, 16) 0 conv1[0][0] ____________________________________________________________________________________________________ conv2 (Conv2D) (None, 17, 17, 16) 2320 max1[0][0] ____________________________________________________________________________________________________ max2 (MaxPooling2D) (None, 8, 8, 16) 0 conv2[0][0] ____________________________________________________________________________________________________ reshape (Reshape) (None, 8, 128) 0 max2[0][0] ____________________________________________________________________________________________________ dense1 (Dense) (None, 8, 32) 4128 reshape[0][0] ____________________________________________________________________________________________________ gru1 (GRU) (None, 8, 512) 837120 dense1[0][0] ____________________________________________________________________________________________________ gru1_b (GRU) (None, 8, 512) 837120 dense1[0][0] ____________________________________________________________________________________________________ add_1 (Add) (None, 8, 512) 0 gru1[0][0] gru1_b[0][0] ____________________________________________________________________________________________________ gru2 (GRU) (None, 8, 512) 1574400 add_1[0][0] ____________________________________________________________________________________________________ gru2_b (GRU) (None, 8, 512) 1574400 add_1[0][0] ____________________________________________________________________________________________________ reshape2 (Reshape) (None, 512, 8) 0 gru2[0][0] ____________________________________________________________________________________________________ reshape2b (Reshape) (None, 512, 8) 0 gru2_b[0][0] ____________________________________________________________________________________________________ concatenate_1 (Concatenate) (None, 1024, 8) 0 reshape2[0][0] reshape2b[0][0] ____________________________________________________________________________________________________ reshape3 (Reshape) (None, 8, 1024) 0 concatenate_1[0][0] ____________________________________________________________________________________________________ dense2 (Dense) (None, 8, 12) 12300 reshape3[0][0] ____________________________________________________________________________________________________ softmax (Activation) (None, 8, 12) 0 dense2[0][0] ====================================================================================================And what coremltools sees:0 : the_input, <keras.engine.topology.InputLayer object at 0x1147c6510> 1 : conv1, <keras.layers.convolutional.Conv2D object at 0x1147c67d0> 2 : conv1__activation__, <keras.layers.core.Activation object at 0x123a6a1d0> 3 : max1, <keras.layers.pooling.MaxPooling2D object at 0x114823a90> 4 : conv2, <keras.layers.convolutional.Conv2D object at 0x114823e50> 5 : conv2__activation__, <keras.layers.core.Activation object at 0x123a6a690> 6 : max2, <keras.layers.pooling.MaxPooling2D object at 0x114823ad0> 7 : reshape, <keras.layers.core.Reshape object at 0x1147c6a50> 8 : dense1, <keras.layers.core.Dense object at 0x11485de10> 9 : dense1__activation__, <keras.layers.core.Activation object at 0x123a6a790> 10 : gru1, <keras.layers.recurrent.GRU object at 0x11484db50> 11 : gru1_b, <keras.layers.recurrent.GRU object at 0x11630a610> 12 : add_1, <keras.layers.merge.Add object at 0x115f9e090> 13 : gru2, <keras.layers.recurrent.GRU object at 0x11f5ead50> 14 : gru2_b, <keras.layers.recurrent.GRU object at 0x116161210> 15 : reshape2, <keras.layers.core.Reshape object at 0x11f475cd0> 16 : reshape2b, <keras.layers.core.Reshape object at 0x12016e8d0> 17 : concatenate_1, <keras.layers.merge.Concatenate object at 0x11f41bd90> 18 : reshape3, <keras.layers.core.Reshape object at 0x120182e50> 19 : dense2, <keras.layers.core.Dense object at 0x11f83d150> 20 : softmax, <keras.layers.core.Activation object at 0x1202f4a10> input { name: "data" type { imageType { width: 35 height: 35 colorSpace: GRAYSCALE } } } input { name: "gru1_h_in" type { multiArrayType { shape: 512 dataType: DOUBLE } isOptional: true } } input { name: "gru1_b_h_in" type { multiArrayType { shape: 512 dataType: DOUBLE } isOptional: true } } input { name: "gru2_h_in" type { multiArrayType { shape: 512 dataType: DOUBLE } isOptional: true } } input { name: "gru2_b_h_in" type { multiArrayType { shape: 512 dataType: DOUBLE } isOptional: true } } output { name: "prediction" type { multiArrayType { shape: 12 dataType: DOUBLE } } } output { name: "gru1_h_out" type { multiArrayType { shape: 512 dataType: DOUBLE } } } output { name: "gru1_b_h_out" type { multiArrayType { shape: 512 dataType: DOUBLE } } } output { name: "gru2_h_out" type { multiArrayType { shape: 512 dataType: DOUBLE } } } output { name: "gru2_b_h_out" type { multiArrayType { shape: 512 dataType: DOUBLE } } }Note: There are three reshape layers in the middle temporarely modify the shape of the layers so that the concatenation goes through the coremltools converter. (Concatenation has to go along a specific axis) May this cause a specific error?Last note: Width and height are flipped during conversion, so I have set the dimensions to be a square (35x35). Do I have to make some rotations to produce a valid input?Any help or suggestion would be appreciated. Thanks!