Neurocle recently launched the latest version of its deep learning vision inspection software, Neuro-T v4.1.
See related content: Neurocle Releases Updated Deep Learning Software for Inspection
The updated software expands features, including its Patch Classification model, which is designed to accurately detect tiny defects without compromising on resolution. This is achieved by segmenting high resolution images into smaller patches. The software also offers an enhanced version of its Defect Generator model capabilities, which utilizes deep learning in manufacturing environments with more limited initial defect data.
Neuro-T 4.1 also includes Neurocle’s shape converter and auto-labeling tools, which transfers box labeling areas into polygon shapes and applies consistent labeling across all images after labeling a few samples.
The software also has expanded processor compatibility, able to support OPENVINO, DirectML, GPU, CPU, NPU, and embedded boards, which brings greater versatility for manufacturers to deploy deep learning models across a wider array of hardware environments.
Neuro-T v4.1 is designed especially for machine vision applications such as defect detection/automated inspection in high-speed production lines, as well as managing limited defect data, and optimizing performance on lightweight embedded devices.
To Learn More:
Contact: Neurocle
Headquarters: Seoul, ROK
Product: Neuro-T v4.1 deep learning software
Key Features: Patch Classification model, shape converter/autolabeling, expanded processor compatibility
What Neurocle says: View more information on Neuro-T v4.1 deep learning software
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