A12: McDefect Solutions: Difference between revisions
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• '''Application in relevant Projects/Initiatives''': N.A. | • '''Application in relevant Projects/Initiatives''': N.A. | ||
• '''Type''': | • '''Type''': Deep Neural Models | ||
• '''AI Breadth''': | • '''AI Breadth''': | ||
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• '''Learning Ability''': Deep Neural Models | • '''Learning Ability''': Deep Neural Models | ||
• '''Related technologies''': | • '''Related technologies''': keras, opencv,sklearn,numpy, matplotlib,fastapi, HTML/CSS/JavaScript | ||
• '''License Information''': | • '''License Information''': Apache License | ||
• '''Related to circularity and sustainability''': No | • '''Related to circularity and sustainability''': No | ||
Revision as of 08:05, 27 April 2023
• Short Description: AI-powered defect detection & classification solutions for industries that cater to the initial stages of manufacturing, using transfer learning by training it for different processes (like casting, rolling, etc.), gaining knowledge from one, and applying it to other processes.
• Reference, URL: https://github.com/tezansahu/McDefect
• Relevant Domain/Industry: Manufacturing
• Application in relevant Projects/Initiatives: N.A.
• Type: Deep Neural Models
• AI Breadth:
• Learning Ability: Deep Neural Models
• Related technologies: keras, opencv,sklearn,numpy, matplotlib,fastapi, HTML/CSS/JavaScript
• License Information: Apache License
• Related to circularity and sustainability: No
• Audience: Manufacturer
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