Why Popular Quantization Guides Miss Critical Threshold Metrics
An analysis of ten prominent quantization guides reveals a significant oversight: the complete absence of threshold-dependent metrics. This gap in evaluation methods can hide critical performance flaws in compressed machine learning models. The article introduces two essential numbers missing from current frameworks that engineers can use to properly evaluate quantization gates and improve overall model reliability.
Source: Towards Data Science