Make RepVGG Greater Again: A Quantization-Aware Approach
Authors: Xiangxiang Chu, Liang Li, Bo Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on detection and semantic segmentation tasks verify its generalization. |
| Researcher Affiliation | Industry | Xiangxiang Chu1, Liang Li1, Bo Zhang1 1Meituan {chuxiangxiang,liliang58,zhangbo97}@meituan.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | We will release the code to facilitate reproduction and future research. |
| Open Datasets | Yes | We mainly focus our experiments on Image Net dataset (Deng et al. 2009). And we verify the generalization of our method based on a recent popular detector YOLOv6 (Li et al. 2022), which extensively adopts the reparameterization design and semantic segmentation. We train and evaluate QARep VGG-fashioned YOLOv6 on the COCO 2017 dataset (Lin et al. 2014). |
| Dataset Splits | Yes | As shown in Table 1, Rep VGG-A0 serevely suffers from large accuracy drop (from 20% to 77% top-1 accuracy) on Image Net validation data-set after standard PTQ. Table 5: Classification results on Image Net validation dataset. |
| Hardware Specification | Yes | Rep VGG and QARep VGG versions are trained for 300 epochs on 8 Tesla-V100 GPUs. |
| Software Dependencies | No | As for PTQ, we use the Py Torch-Quantization toolkit (NVIDIA 2018), which is widely used in deployment on NVIDIA GPUs. |
| Experiment Setup | Yes | All models are trained for 10 epochs (the first three ones for warm-up) with an initial learning rate of 0.01. Rep VGG and QARep VGG versions are trained for 300 epochs on 8 Tesla-V100 GPUs. All models are trained using crop size of 512 1024. |