Using a Deep Learning Tool

Deep Learning Studio

  • Include the new images in the test set

    • Add new images to the dataset from the Dataset and annotations tab.

    • In the Validation and results tab, refresh the results

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    Refreshing or computing new results

  • Use the Inference tests tab to classify the new images and display the detailed results for these images.

    • You can also export the results to CSV from this tab.

API

  • The API to use a Deep Learning tool on a new image or on a set of images is different for each tool:

  • You can apply each tool to individual images or to sets of images.

    • With a set of images and with GPU processing enabled, the tool processes EDeepLearningTool::GetBatchSize .NET Python at the same time.

    • EasySegment can automatically split individual images in sub-images according to the patch size, the scaling and the sampling density parameters. These sub-images are processed by batch.

    • Use the methods EDeepLearningTool::InitializeInference .NET Python with an image or a set of images to initialize the inference engine after changing the batch size, the execution settings, or loading a model. Otherwise, the initialization will be performed on the first prediction. The initialization can take a significant amount of time.