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IEEE/ICACT20260190 Slide.17        [Big Slide]	Oral Presentation
	Looking at the detailed comparison between the two models: PaDiM uses pre-trained CNN features with a Mahalanobis anomaly map. It offers fast inference speed, low memory and hardware requirements, and medium accuracy. However, it does not support multi-class scenarios. In contrast, Dinomaly uses ViT and Transformer features with loosely enforced reconstruction. While its inference speed is slower and it requires higher memory and hardware resources, it achieves high accuracy and supports multi-class environments, making it more suitable for diverse production lines.

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