Abstract: To quantitatively evaluate an image binarization algorithm when the ground-truth binary images are unavailable, the authors propose a quantitative evaluation approach based on pooling. First ...
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Abstract: This paper presents BAM-Net, a hardware-efficient binarization algorithm designed for associative memory (AM) implementation. BAM-Net aims to reduce memory overhead, power consumption, and ...
Train the model using Quantization-Aware Training (QAT) with the RaBiT approach. The codebase uses a generic RaBiTModel wrapper that works with any HuggingFace AutoModelForCausalLM architecture — no ...