class PredictedImageData
        {
            [ColumnName("box")]
            [VectorType(1)]
            public float[] BoundingBox { get; set; }

            [ColumnName("landmarks")]
            [VectorType(1)]
            public float[] Landmarks { get; set; }

            [ColumnName("prob")]
            public float Probability { get; set; }
        }

        private struct ImageNetSettings
        {
            public const int imageHeight = 224;
            public const int imageWidth = 224;
        }

        public class ImageNetData
        {
            public string ImagePath;

            //[ImageType(ImageNetSettings.imageHeight, ImageNetSettings.imageWidth)]
            //[VectorType(1)]
            //[ColumnName("input")]
            //public Bitmap Input { get; set; }

            [VectorType(1)]
            [ColumnName("min_size")]
            public float[] MinSize;

            [VectorType(1)]
            [ColumnName("factor")]
            public float[] Factor;

            [VectorType(3)]
            [ColumnName("thresholds")]
            public float[] Thresholds;
        }

        static void Main(string[] args)
        {
            var mlContext = new MLContext(seed: 1);
            var modelLocation = "mtcnn.pb";
            var data = mlContext.Data.LoadFromEnumerable(new List<ImageNetData>());

            var pipeline = mlContext
                .Transforms.LoadImages(
                    outputColumnName: "input",
                    imageFolder: "",
                    inputColumnName: nameof(ImageNetData.ImagePath))
                .Append(mlContext
                .Transforms.ResizeImages(
                    outputColumnName: "input",
                    imageWidth: ImageNetSettings.imageWidth,
                    imageHeight: ImageNetSettings.imageHeight,
                    inputColumnName: "input"))
                .Append(mlContext.Transforms.ExtractPixels(
                    outputColumnName: "input"))
                .Append(mlContext.Model.LoadTensorFlowModel(modelLocation).ScoreTensorFlowModel(
                    inputColumnNames: new[] { "input", "thresholds", "min_size", "factor" },
                    //outputColumnNames: new[] { "box", "prob", "landmarks" },
                    outputColumnNames: new[] { "box" },
                    addBatchDimensionInput: true));

            ITransformer model = pipeline.Fit(data);

            var predictionEngine = mlContext.Model.CreatePredictionEngine<ImageNetData, PredictedImageData>(model);

            var prediction = predictionEngine.Predict(new ImageNetData
            {
                ImagePath = "anastasia3.jpg",
                //Input = (Bitmap)Image.FromFile("anastasia3.jpg"),
                MinSize = new float[] { 40F },
                Factor = new float[] { 0.709F },
                Thresholds = new float[] { 0.6F, 0.7F, 0.7F }
            });
        }