ML.net Modelbuilder for object detection

Eric Fredericksen 1 Reputation point
2021-10-11T04:49:17.683+00:00

I am attempting to build a model using my own data with modelbuilder to detect specific objects in images. I get to the training step and get an exception. I am using VOTT JSON data. I have tried several times but continue to get this same error.

Error description : Unable to split the file provided into multiple, consistent columns. Readable formats include delimited files such as CSV/TSV. Check for a consistent number of columns and proper escaping and quoting.

Exception:
at Microsoft.ML.AutoML.ColumnInferenceApi.InferSplit(MLContext context, TextFileSample sample, Nullable1 separatorChar, Nullable1 allowQuotedStrings, Nullable1 supportSparse) at Microsoft.ML.AutoML.ColumnInferenceApi.InferColumns(MLContext context, String path, ColumnInformation columnInfo, Nullable1 separatorChar, Nullable1 allowQuotedStrings, Nullable1 supportSparse, Boolean trimWhitespace, Boolean groupColumns, Boolean hasHeader)
at Microsoft.ML.AutoML.ColumnInferenceApi.InferColumns(MLContext context, String path, String labelColumn, Nullable1 separatorChar, Nullable1 allowQuotedStrings, Nullable1 supportSparse, Boolean trimWhitespace, Boolean groupColumns) at Microsoft.ML.ModelBuilder.AutoMLEngine.<StartTrainingAsync>d__21.MoveNext() in /_/src/Microsoft.ML.ModelBuilder.AutoMLService/AutoMLEngineService/AutoMLEngine.cs:line 150 at StreamJsonRpc.JsonRpc.<InvokeCoreAsync>d__1391.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task)
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at System.Runtime.CompilerServices.TaskAwaiter.ValidateEnd(Task task)
at Microsoft.ML.ModelBuilder.ViewModels.TrainViewModel.<<StartTrainingAsync>b__109_0>d.MoveNext()

.NET Machine learning
.NET Machine learning
.NET: Microsoft Technologies based on the .NET software framework.Machine learning: A type of artificial intelligence focused on enabling computers to use observed data to evolve new behaviors that have not been explicitly programmed.
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