Deep Learning - AI

With these tools, you can use deep learning technology to categorize images. The term deep learning (DL) refers to a family of machine learning methods. MERLIC provides tools for the methods classification and anomaly detection. The distinguishing feature is either an anomaly or a previously defined class.

License Requirements

To use the MERLIC tools of the "Deep Learning - AI" category, one of the following license criteria must be fulfilled:

You have a license for the MERLIC package "X-Large".
You have a license for one of the other MERLIC packages ("Small", "Medium", "Large") and an additional license for the add-on "Deep Learning". The number of purchased deep learning add-ons determines how many MERLIC tools of the category Deep Learning - AI you may use in an MVApp:
- If only one add-on was purchased and activated, only one Deep Learning - AI tool is allowed per MVApp.
- If two add-ons were purchased and activated, the number of Deep Learning - AI tools is not restricted.

Support of AI Accelerator Hardware

MERLIC comes with Artificial Intelligence Acceleration Interfaces (AI²) for the NVIDIA® TensorRT™ SDK and the Intel® Distribution of OpenVINO™ toolkit. They enable you to use AI accelerator hardware that is compatible with the NVIDIA® TensorRT™ SDK or the OpenVINO™ toolkit to optimize deep learning models for inference in MERLIC tools with deep learning functionality. As a result, significantly faster deep learning inference times can be achieved on NVIDIA® GPUs and on Intel® processors including CPUs, Intel® GPUs, and Intel® VPUs.

To get more general information about the AI² interface, e.g., the system requirements and how to use hardware that can be accelerated via the OpenVINO™ toolkit or NVIDIA® TensorRT™ in MERLIC, see the topic AI² Interfaces for Tools with Deep Learning.

Common Use Cases

Use this overview to find the right deep learning tool.

Task	Tool	Usage
Classify an entire image into one class out of a given set of classes. Use this tool if you have a data set where all classes are represented equally and enough data for all classes is present. The classification model can learn the features explicitly during training. Thus, its highly likely that this method will perform better than an anomaly detection.	Classify Image	For example, differentiate "good" samples and "bad" samples. In order to do this, you must first teach the deep learning model which images belong to the "good" category and which images belong to the "bad" category. To teach the neural network, use the MVTec Deep Learning Tool. The workflow is as follows: Define classes, label your images accordingly, and finally, train the deep learning model. The training will result in a so-called classifier. Import this classifier into MERLIC and apply it to new images. These images will then be categorized into the previously defined classes.
Assign to each pixel the likelihood that it shows an unknown feature. A score is assigned to every pixel of the input image, indicating how likely it shows an unknown feature, i.e., a structural anomaly. Use this tool to find structural anomalies in images in order to differentiate between good samples, i.e., images without defect, and bad samples, i.e., images with defect. This tool is a good solution if you have only few images or you don't know how the defects will appear later on, because you only need good samples to train the model.	Detect Anomalies	To find anomalies, you first need to train the deep learning model. It is sufficient to train how good samples (without anomalies) look like. Bad samples are optional but can help to improve the model. After the training, the deep learning model will be able to decide whether new images have a defect or not and where in the image the defect is located.
Assign to each pixel the likelihood that it shows either an unknown feature or that it violates constraints regarding the image content. A score is assigned to every pixel of the input image, indicating how likely it shows an unknown feature on a smaller scale, i.e., a structural anomaly, or a constraint violation regarding the image content on a larger scale, i.e., a logical anomaly. Use this tool to find structural and logical anomalies in images in order to differentiate between good samples, i.e., images without defect, and bad samples, i.e., images with defect. This tool is a good solution if you are searching for logical anomalies on a large scale. However, it takes a lot of images to train the deep learning model used to detect anomalies in a global context. As long as a large enough data set is available, this should be no problem, as the MVTec Deep Learning Tool is designed to work with many images. Thus, if you have a large data set to train the model, the tool "Detect Anomalies in the Global Context" could also be your choice when looking for structural anomalies, as it is much easier to train a deep learning model with lots of images using the MVTec Deep Learning Tool instead of the training mode in the MERLIC tool "Detect Anomalies".	Detect Anomalies in the Global Context	To find anomalies in the global context, you first need to train a deep learning model with the MVTec Deep Learning Tool or MVTec HALCON. Import this deep learning model into MERLIC and apply it to new images. The deep learning model will be able to decide whether new images have a defect or not and where in the image the defect is located.
Locate various objects within one image and classify them into classes out of a given set of classes. Use this tool if you have a data set where all classes are represented equally and if enough data for all classes are present. The object detection model can learn the features explicitly during training.	Find Objects	To locate objects within an image and classify them into classes, you first need to train an object detection model with the MVTec Deep Learning Tool or MVTec HALCON. Import this object detection model into MERLIC and apply it to new images. The object detection model will be able to recognize objects of the given classes and mark where in the image the objects are located.