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GTC ON-DEMAND

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Abstract:
We'll explain how to deploy a weakly supervised deep learning accelerator solution for end-to-end industrial inspection in the framework of vision-based anomaly detection. Compared with other computer vision problems, deploying a deep learning-based industrial inspection solution in production runs into three fundamental limiters scarcity of curated data, extreme low quantity of defects, and multi-scale defect sizes. We will describe how to overcome these by using weakly supervised transfer learning that's based on award-winning U-net architecture. We also provide a deep dive into concepts by using examples that include data curation, verification, and end-to-end deployment based on an NVIDIA GPU Cloud development environment with the latest versions of CUDA and cuDNN, as well as GPU-Accelerated Tensorflow and Keras.
We'll explain how to deploy a weakly supervised deep learning accelerator solution for end-to-end industrial inspection in the framework of vision-based anomaly detection. Compared with other computer vision problems, deploying a deep learning-based industrial inspection solution in production runs into three fundamental limiters scarcity of curated data, extreme low quantity of defects, and multi-scale defect sizes. We will describe how to overcome these by using weakly supervised transfer learning that's based on award-winning U-net architecture. We also provide a deep dive into concepts by using examples that include data curation, verification, and end-to-end deployment based on an NVIDIA GPU Cloud development environment with the latest versions of CUDA and cuDNN, as well as GPU-Accelerated Tensorflow and Keras.  Back
 
Topics:
Industrial Inspection
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9291
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