Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving
Authors: Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Wangmeng Xiang, Binghui Chen, Bin Luo, Yifeng Geng, Xuansong Xie
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our testing shows that DAMO-Stream Net surpasses current state-of-the-art methodologies, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200, 1920)) s AP without requiring additional data. Our experiments demonstrate that DAMO-Stream Net surpasses existing SOTA methods, achieving 37.8% (normal size (600, 960)) and 43.3% (1200, 1920)) s AP without utilizing any extra data. |
| Researcher Affiliation | Collaboration | Jun-Yan He1 , Zhi-Qi Cheng2 , Chenyang Li1 , Wangmeng Xiang1 , Binghui Chen1 , Bin Luo1 , Yifeng Geng1 , Xuansong Xie1 1DAMO Academy, Alibaba Group 2Carnegie Mellon University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is at https://github.com/ zhiqic/DAMO-Stream Net. |
| Open Datasets | Yes | We utilized the Argoverse-HD dataset, which comprises various urban outdoor scenes from two US cities. We pretrained the base detector of our DAMO-Stream Net on the COCO dataset [Lin et al., 2014], following the methodology of Stream YOLO [Yang et al., 2022a]. |
| Dataset Splits | Yes | We adhered to the train/validation split proposed by Li et al. [Li et al., 2020], with the validation set consisting of 15k frames. |
| Hardware Specification | Yes | We then trained DAMO-Stream Net on the Argoverse-HD dataset for 8 epochs with a batch size of 32, using 4 V100 GPUs. Table 6: Ablation study of inference time (ms) on V100. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies like Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | We then trained DAMO-Stream Net on the Argoverse-HD dataset for 8 epochs with a batch size of 32, using 4 V100 GPUs. The normal input resolution (600, 960) was utilized unless specified otherwise. AK-Distillation is an auxiliary loss for DAMO-Stream Net training, with the weight of the loss set to 0.2/0.2/0.1 for DAMO-Stream Net S/M/L, respectively. |