Figure 1 shows the defect mask prediction using the trained model.įigure 1. ![]() Using the TAO Toolkit, we use transfer learning to train a model that achieves an overall accuracy of 99.67%, 92.3% mIoU, 95.8% mF1, 97.5 mPrecision, and 94.3% mRecall on the bottle class of the MVTec Anomaly dataset. This comprehensive benchmarking dataset is designed for anomaly detection in machine vision, consisting of various industrial products with both normal and defective samples. In this post, we leverage an advanced pretrained model for change detection called VisualChangeNet and fine-tune it with the TAO Toolkit to detect defects in the MV Tech Anomaly detection dataset. With the TAO Toolkit, developers can use pretrained models and fine-tune them for specific use cases. It simplifies and accelerates the model training process by abstracting away the complexity of AI models and deep learning frameworks. NVIDIA TAO Toolkit is a low-code AI toolkit built on TensorFlow and PyTorch. This post explores how NVIDIA TAO can be employed to design custom AI models that pinpoint defects in industrial applications, enhancing overall quality. According to the American Society of Quality, “Many organizations will have true quality-related costs as high as 15-20% of sales revenue, some going as high as 40% of total operations.” These staggering statistics reveal a stark reality: defects in industrial applications not only jeopardize product quality but also drain a significant portion of a company’s revenue.īut what if companies could reclaim these lost profits and channel them back into innovation and expansion? This is where the potential of AI shines. Efficiency is paramount in industrial manufacturing, where even minor gains can have significant financial implications.
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