MixFrag

MixFrag: Fragility-guided mixed-precision post-training quantization for Vision Transformers

MixFrag is a new post-training quantization (PTQ) framework for Vision Transformers that assigns mixed bit-widths per layer based on quantization fragility, measured via KL divergence between full-precision and quantized output distributions. Bit allocation is formulated as a Multiple-Choice Knapsack Problem to optimize precision under a target bit budget. Evaluated on ImageNet-1K classification and COCO detection/segmentation, MixFrag claims state-of-the-art among mixed-precision PTQ methods, improving the prior best by up to 9.6 AP on a challenging low-bit setting.