The Scala object containing the main() method has the following workflow:

1. We read all the business labels from the train.csv file
2. We read and create a map from image ID to business ID of form imageID → busID
3. We get a list of images from the photoDir directory to load and process and, finally, get the image IDs of 10,000 images (feel free to set the range)
4. We then read and process images into a photoID → vector map
5. We chain the output of step 3 and step 4 to align the business feature, image IDs, and label IDs to get the feature extracted for the CNN
6. We construct nine CNNs.
7. We train all the CNNs and specify the model savings locations
8. We then repeat step 2 to step 6 to extract the features from the test set
9. Finally, we evaluate the model and save the prediction in a CSV file

Now let's see how the preceding steps would look in a high-level diagram:

Programmatically, the preceding steps can be represented as follows:

val labelMap = readBusinessLabels("data/labels/train.csv")
val businessMap = readBusinessToImageLabels("data/labels/train_photo_to_biz_ids.csv")
val imgs = getImageIds("data/images/train/", businessMap, businessMap.map(_._2).toSet.toList).slice(0,100) // 20000 images

println("Image ID retreival done!")
val dataMap = processImages(imgs, resizeImgDim = 128)
println("Image processing done!")
val alignedData = new featureAndDataAligner(dataMap, businessMap, Option(labelMap))()

println("Feature extraction done!")
val cnn0 = trainModelEpochs(alignedData, businessClass = 0, saveNN = "models/model0")
val cnn1 = trainModelEpochs(alignedData, businessClass = 1, saveNN = "models/model1")
val cnn2 = trainModelEpochs(alignedData, businessClass = 2, saveNN = "models/model2")
val cnn3 = trainModelEpochs(alignedData, businessClass = 3, saveNN = "models/model3")
val cnn4 = trainModelEpochs(alignedData, businessClass = 4, saveNN = "models/model4")
val cnn5 = trainModelEpochs(alignedData, businessClass = 5, saveNN = "models/model5")
val cnn6 = trainModelEpochs(alignedData, businessClass = 6, saveNN = "models/model6")
val cnn7 = trainModelEpochs(alignedData, businessClass = 7, saveNN = "models/model7")
val cnn8 = trainModelEpochs(alignedData, businessClass = 8, saveNN = "models/model8")

val businessMapTE = readBusinessToImageLabels("data/labels/test_photo_to_biz.csv")

val imgsTE = getImageIds("data/images/test//", businessMapTE, businessMapTE.map(_._2).toSet.toList)

val dataMapTE = processImages(imgsTE, resizeImgDim = 128) // make them 128*128

val alignedDataTE = new featureAndDataAligner(dataMapTE, businessMapTE, None)()
val Results = SubmitObj(alignedDataTE, "results/ModelsV0/")
val SubmitResults = writeSubmissionFile("kaggleSubmitFile.csv", Results, thresh = 0.9)

Too much of a mouthful? Don't worry, we will now see each step in detail. If you look at the preceding steps carefully, you'll see steps 1 to step 5 are basically image processing and feature constructions.