1.58bit detr

우병주·2024년 11월 8일
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  1. q-detr의 distribution alignment 은 뭔가 1.58bit detr에도 도움 되지 않을까?
  2. detrdistill 논문에 있는 3가지 요소를 잘 써보면 괜찮을 듯
    (1+2): IB principle에 맞게, DA module로 self information entropy는 maximization하면서도 student와 teacher query의 conditional entropy는 minimization하도록 할때, minimization 방법으로써 detrdistill 방법론을 사용해도 무방할듯 하다. (foreground matching 약간 어지러움)
  3. 현재 지금 STE인가? gradient 근사하는 기법이 적용된 상태인 것인지?
  4. resnet-50이 Imagenet pre-trained인가?
  5. bitnet 논문에 나와있듯 learning rate를 올려도 괜찮지 않을까?

Quantization, indeed, aims to reduce model size and improve efficiency, but its limited SOTA achievements in real-time object detection compared to frameworks like YOLO and RT-DETR are rooted in specific challenges and trade-offs. Here are key reasons, supported by recent findings:

Accuracy vs. Latency Trade-off: Quantization techniques, especially lower-bit quantization, often reduce model accuracy due to information loss, particularly in high-complexity tasks like object detection. Object detectors need precise localization and classification, which suffer under lower precision. Studies like that of Banner et al. (2019) and Jain et al. (2021) have shown that quantization tends to degrade bounding box regression and classification accuracy in detection tasks more than in simpler tasks like image classification of Detection Tasks**: Object detection demands more feature extraction and processing than classification, and quantized models typically face challenges with feature maps and bounding box regressions at lower bit-widths. As a result, the drop in detection performance is more pronounced than in tasks with less spatial and semantic demand .

Real-timets and Model Compatibility: Many real-time detectors, especially those like RT-DETR, are highly optimized for specific architectures and hardware setups. Quantization might not translate well to these specialized designs and could even lead to inefficiencies that cancel out intended latency gains. Research by Elharrouss et al. (2021) highlights how real-time requirements and optimization techniques limit the scope for applying extensive quantization while maintaining performance .

Hardware Limitatiopatibility: Although some hardware (like TPUs) is optimized for quantized models, many GPUs lack robust support for very low-bit inference. Without compatible hardware, quantized real-time detectors may actually see increased inference time due to emulated low-bit operations rather than true speedups .

In summary, quantization's impacacy, detection complexity, and hardware compatibility all contribute to its limited adoption and SOTA performance in real-time object detection.

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