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 | Conference PDF | Archive PDF | Plain Text | 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. |