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ImageClassification.Predict
ImageClassification.Train
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README.md

README.md

Image Classification

ML.NET version API type Status App Type Data type Scenario ML Task Algorithms
v1.3.1 Dynamic API Up-to-date Console app .tsv + image files Image classification featurization + classification deep neural network + SDCA

Problem

Image classification is a common problem which has been solved quite a while using Machine Learning techniques. In this sample, we will review an approach that mixes new techniques (deep learning) and old school (SDCA) techniques.

In this model, we use the Inception model as a featurizer (the model is already stored in the assets folder ). This means that the model will process input images through the neural network, and then it will use the output of the tensor which precedes the classification. This tensor contains the image features, which allows to identify an image.

Finally, these image features will be feed to an SDCA algorithm which will learn how to classify different sets of image features.

DataSet

There are two data sources: the tsv file and the image files. The tsv file contains two columns: the first one is defined as ImagePath and the second one is the Label corresponding to the image. As you can observe, the file does not have a header row, and looks like this:

broccoli.jpg	broccoli
pizza.jpg	pizza
pizza2.jpg	pizza
teddy2.jpg	teddy
teddy3.jpg	teddy
teddy4.jpg	teddy
toaster.jpg	toaster
toaster2.png	toaster

The training and testing images are located in the assets folders. These images belong to Wikimedia Commons.

Wikimedia Commons, the free media repository. Retrieved 10:48, October 17, 2018 from:
https://commons.wikimedia.org/wiki/Pizza
https://commons.wikimedia.org/wiki/Toaster
https://commons.wikimedia.org/wiki/Teddy_bear

ML Task - Image Classification

To solve this problem, first we will build an ML model. Then we will train the model on existing data, evaluate how good it is, and lastly we'll consume the model to classify a new image.

1. Build Model

Building the model includes the following steps:

  • Loading the tsv file
  • Image loading and transformation (resize and normalize pixel values, as required by the deep neural network)
  • Image featurization using the deep neural network
  • Image classification using SDCA

Define the schema of data in a class type and refer that type while loading data using TextLoader. Here the class type is ImageNetData.

[<CLIMutable>]
type ImageNetData = 
    {
        [<LoadColumn(0)>]
        ImagePath : string
        [<LoadColumn(1)>]
        Label : string
    }

Load the training data using Text loader

let data = mlContext.Data.ReadFromTextFile<ImageNetData>(dataLocation, hasHeader = false)

The following step is to define the estimator pipe. Usually, when dealing with deep neural networks, you must adapt the images to the format expected by the network. This is the reason images are resized and then transformed (mainly, pixel values are normalized across all R,G,B channels).

let pipeline =
    EstimatorChain()
        .Append(mlContext.Transforms.Conversion.MapValueToKey("LabelTokey", "Label"))
        .Append(mlContext.Transforms.LoadImages(imagesFolder, struct ("ImageReal", "ImagePath")))
        .Append(mlContext.Transforms.Resize("ImageReal", imageWidth, imageHeight, inputColumnName = "ImageReal"))
        .Append(mlContext.Transforms.ExtractPixels(ImagePixelExtractorTransformer.ColumnInfo("input", "ImageReal", interleave = channelsLast, offset = float32 mean)))
        .Append(mlContext.Transforms.ScoreTensorFlowModel(inputModelLocation, [| "softmax2_pre_activation" |], [| "input" |]))
        .Append(mlContext.MulticlassClassification.Trainers.LogisticRegression("LabelTokey", "softmax2_pre_activation"))
        .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"))

2. Train model

In order to begin the training execute Fit on the built pipeline:

let model = pipeline.Fit(data)

As a reference, In the following screenshot, you can check the DataView used to train the SDCA; this DataView includes the property named softmax2_pre_activation (also known as image features), which content is produced by the ApplyTensorFlowGraph function.

3. Evaluate model

After the training, we evaluate the model using the training data. The Evaluate function needs a IDataView as parameter, so we apply Transform to the model, and then take the AsDynamic value.

printHeader ["Classification metrics"]
let metrics = mlContext.MulticlassClassification.Evaluate(trainData, label = "LabelTokey", predictedLabel = "PredictedLabel")
printfn "LogLoss is: %.15f" metrics.LogLoss
metrics.PerClassLogLoss
|> Seq.map string
|> String.concat " , "
|> printfn "PerClassLogLoss is: %s"

Finally, we save the model:

do 
    use f = File.OpenWrite(outFile)
    mlContext.Model.Save(model, f)

Model training

You should proceed as follows in order to train a model your model:

  1. Set ImageClassification.Train as starting project in Visual Studio
  2. Press F5 in Visual Studio. After some seconds, the process will finish and the output should be similar to the following screenshot:

4. Consume model

First, we need to load the model created during Model training

let loadedModel = 
    use f = File.OpenRead(modelLocation)
    mlContext.Model.Load(f)

Then, we proceed to create a predictor function, and make predictions:

let predictor = loadedModel.CreatePredictionEngine<ImageNetData,ImageNetPrediction>(mlContext
let pred = predictor.Predict(testImage);

The prediction function receives as parameter an object of type ImageNetData (containing 2 properties: ImagePath and Label). Then returns and object of type ImagePrediction, which holds the PredictedLabel and Score (probability value between 0 and 1) properties.

Model testing

  1. Copy the model produced by the training model (located at ImageClassification.Train/assets/outputs/imageClassifier.zip ) to the prediction project (at ImageClassification.Predict/assets/inputs/imageClassifier.zip ).
  2. Set VS default startup project: Set ImageClassification.Predict as starting project in Visual Studio.
  3. Press F5 in Visual Studio. After some seconds, the process will finish and the output should be similar to the following screenshot:
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