# EasySegmentUnsupervisedTrain

| Support            | ![](assets/images/sample_programs/applies_to/cpp.svg) ![](assets/images/sample_programs/applies_to/cs.svg)   |
|--------------------|--------------------------------------------------------------------------------------------------------------|
| Required licenses  | **EasySegment**                                                                                              |
| Recommended images | n/a                                                                                                          |
| Location           | `Deep Learning Inspection/EasySegmentUnsupervisedTrain`                                                      |

## Purpose

This sample program demonstrates how to:

- Train an **EasySegment Unsupervised** tool in console mode.

## Code highlights

By default, the sample program trains a tool for the dataset `Fabric` in the Deep Learning Additional Resources.

#### NOTE
In the code, you need to replace `DEEP_LEARNING_ADDITIONAL_RESOURCES` by the path of the Deep Learning Additional Resources.

1. Select the first available GPU for the training.
   ```c++
   // initialize a segmenter that will be trained
   EUnsupervisedSegmenter newUnsupervisedSegmenter;

   // make sure that we have a GPU to use (note: GPU's can only be detected in x64 configuration)
   std::vector<EDeepLearningDevice> devices = newClassifier.GetAvailableDevices();

   bool foundGPU = false;
   for (size_t i = 0; i < devices.size(); i++)
   {
     if (devices[i].GetDeviceType() == EDeepLearningDeviceType_GPU)
     {
       newClassifier.SetActiveDevice(devices[i]);
       foundGPU = true;
       break;
     }
   }
   ```
2. Load a dataset from a **Deep Learning Studio** project.
   ```c++
   const EClassificationDataset* dataset;
   EDeepLearningProject project;
   project.Load(PROJECT_PATH);
   dataset = &project.GetDataset();
   ```
3. Alternatively, load a dataset directly using a `.edldataset` file.
   ```c++
   const EClassificationDataset* dataset;
   EClassificationDataset datasetHolder;
   datasetHolder.Load(DATASET_PATH);
   dataset = &datasetHolder;
   ```
4. Split the dataset in a training part and a validation part.

#### NOTE
Be careful that the method for splitting a dataset is different for each type of tool. **EasySegment Unsupervised** uses the same method as **EasyClassify** to create a split.

```none
```c++
// Put 80% of images into the training dataset and 20% in the validation dataset
EClassificationDataset trainingDataset;
EClassificationDataset validationDataset;
dataset->SplitDataset(trainingDataset, validationDataset, 0.8f);
```
```

1. Configure and train the **EasySegment Unsupervised** tool and wait for the training to end.
   ```c++
   newUnsupervisedSegmenter.SetCapacity(EUnsupervisedSegmenterCapacity_Small);
   newUnsupervisedSegmenter.SetForceGrayscale(true);
   newUnsupervisedSegmenter.SetGoodLabel("Good");
   newUnsupervisedSegmenter.Train(trainingDataset, validationDataset, 50);
   newUnsupervisedSegmenter.WaitForTrainingCompletion();
   ```
2. Check the validation accuracy of the tool we just trained.
   ```c++
   float newUnsupervisedSegmenterAccuracy = newUnsupervisedSegmenter.GetValidationMetrics(newUnsupervisedSegmenter.GetBestIteration()).GetBestAccuracy();
   std::cout << "New segmenter Accuracy:" << newUnsupervisedSegmenterAccuracy << std::endl;
   ```