# Cognex VisionPro Deep Learning Tools

The Cognex VisionPro Deep Learning tools are a collection of machine vision tools which tackle different hard to program challenges through the use of deep learning. While the tools share a common engine, they differ in what they are looking for in images. More specifically, each tool has a different focus analyzing either single points, individual regions or complete images:

| The Blue Locate Tool is used to find and localize single or multiple features within an image. From small features on noisy backgrounds to complex objects in bulk; the Blue Locate tool can localize and identify complex features and objects by learning from annotated images. To train the Blue Locate tool, all you need to provide are images where the targeted features are marked.
| The Blue Read Tool is used to perform Optical Character Recognition (OCR) within an image. From cleanly printed characters to strongly deformed characters on very noisy backgrounds, the Blue Read tool can identify and read characters by learning from annotated images. To train the Blue Read tool, all you need to provide are images where the targeted characters are marked.
| There are 3 types of architecture in Red Analyze Tool, Focused modes (Focused Supervised, Focused Unsupervised), High Detail mode, and High Detail Quick mode. The Red Analyze Tool – Supervised Mode (Focused) is used to segment specific regions such as defects or other areas of interest. Be it blowholes in cast metal or bruised vegetables on a conveyor; the Red Analyze tool in Supervised mode can identify all of these and many more problems simply by learning the varying appearance of the defect or target region. To train the Red Supervised tool, all you need to provide are images of the type of defective regions you are looking for.
The Red Analyze Tool – Unsupervised Mode (Focused) is used to detect anomalies and aesthetic defects. Be it scratches on a decorated surface, incomplete or improper assemblies or even weaving problems in textiles; the Red Analyze tool can identify all of these and many more problems simply by learning the normal appearance of an object including its significant but tolerable variations. To train the Red Unsupervised tool, all you need to provide are images of good objects.
The Red Analyze Tool – High Detail Mode is the reinforced version of Red Analyze Focused – Supervised in terms of segmentation performance but costs some drop in training and processing speed. Its higher performance compared to Red Analyze Focused – Supervised comes from its unique training architecture in segmentation tasks.
| The Red Analyze Tool – High Detail Quick Mode is the modification of Red Analyze High Detail mode, hiking up its training speed dramatically with little decrease in detection accuracy. It was forged to import the state-of-the-art training algorithms to guarantee robust and performing results. It also requires few Tool Parameters thus offers easy and quick training. The other details including its step-by-step usage are not much different from Red Analyze High Detail.
| There are 3 types of architecture in Green Classify Tool, Focused, High Detail mode, and High Detail Quick mode. The Green Classify Tool – Focused Mode is used to classify an object or a complete scene. Be it the identification of products based on their packaging, the classification of welding seams or the separation of acceptable or unacceptable defects; the Focused mode learns to separate different classes based on a collection of labeled images. To train the Focused mode, all you need to provide are images assigned and labeled the different classes.
The Green Classify Tool – High Detail Mode is similar with Focused Green Classify Tooll, but uses a different architecture. The High Detail mode learns to separate different classes based on a collection of labeled images. To train the High Detail mode, all you need to provide are images assigned and labeled the different classes.
The Green Classify Tool – High Detail Quick Mode is the modification of Green Classify High Detail mode, hiking up its training speed dramatically with some decrease in detection accuracy. It was forged to import the state-of-the-art training algorithms to guarantee robust and still performing results though its accuracy level is slightly lower than that of Green Classify High Detail. It also requires few Tool Parameters thus offers easy and quick training. The other details including its step-by-step usage are not much different from Green Classify High Detail.