# StreamPETR **Repository Path**: flashdxy/StreamPETR ## Basic Information - **Project Name**: StreamPETR - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-01-15 - **Last Updated**: 2024-05-29 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

StreamPETR

[ICCV2023] Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/exploring-object-centric-temporal-modeling/3d-multi-object-tracking-on-nuscenes-camera-1)](https://paperswithcode.com/sota/3d-multi-object-tracking-on-nuscenes-camera-1?p=exploring-object-centric-temporal-modeling) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/exploring-object-centric-temporal-modeling/3d-object-detection-on-nuscenes-camera-only)](https://paperswithcode.com/sota/3d-object-detection-on-nuscenes-camera-only?p=exploring-object-centric-temporal-modeling) [![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2303.11926)

## Introduction This repository is an official implementation of StreamPETR. ## News - [2023/07/14] StreamPETR is accepted by ICCV 2023. - [2023/05/03] StreamPETR-Large is the first online multi-view method that achieves comparable performance (62.0 mAP, 67.6 NDS and 65.3 AMOTA) with the baseline of lidar-based method. ## Getting Started Please follow our documentation step by step. If you like our work, please recommend it to your colleagues and friends. 1. [**Environment Setup.**](./docs/setup.md) 2. [**Data Preparation.**](./docs/data_preparation.md) 3. [**Training and Inference.**](./docs/training_inference.md) ## Model Zoo

## Results on NuScenes Val Set. | Model | Setting |Pretrain| Lr Schd | Training Time | NDS| mAP|FPS-pytorch | Config | Download | | :---: | :---: | :---: | :---: | :---:|:---:| :---: | :---: | :---: | :---: | |StreamPETR| V2-99 - 900q | [FCOS3D](https://github.com/exiawsh/storage/releases/download/v1.0/fcos3d_vovnet_imgbackbone-remapped.pth) | 24ep | 13 hours | 57.1 | 48.2 | 12.5 |[config](projects/configs/StreamPETR/stream_petr_vov_flash_800_bs2_seq_24e.py) |[model](https://github.com/exiawsh/storage/releases/download/v1.0/stream_petr_vov_flash_800_bs2_seq_24e.pth)/[log](https://github.com/exiawsh/storage/releases/download/v1.0/stream_petr_vov_flash_800_bs2_seq_24e.log) | RepDETR3D| V2-99 - 900q | [FCOS3D](https://github.com/exiawsh/storage/releases/download/v1.0/fcos3d_vovnet_imgbackbone-remapped.pth) | 24ep | 13 hours | 58.4 | 50.1 | 13.1 |[config](projects/configs/RepDETR3D/repdetr3d_vov_800_bs2_seq_24e.py) |[model](https://github.com/exiawsh/storage/releases/download/v1.0/repdetr3d_vov_800_bs2_seq_24e.pth)/[log](https://github.com/exiawsh/storage/releases/download/v1.0/repdetr3d_vov_800_bs2_seq_24e.log) | |StreamPETR| R50 - 900q | ImageNet | 90ep | 36 hours | 53.7 | 43.2 | 26.7 |[config](projects/configs/StreamPETR/stream_petr_r50_flash_704_bs2_seq_90e.py) |[model](https://github.com/exiawsh/storage/releases/download/v1.0/stream_petr_r50_flash_704_bs2_seq_90e.pth)/[log](https://github.com/exiawsh/storage/releases/download/v1.0/stream_petr_r50_flash_704_bs2_seq_90e.log) | |StreamPETR| R50 - 428q | [NuImg](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim_20201009_124951-40963960.pth) | 60ep | 26 hours | 54.6 |44.9 | 31.7 |[config](projects/configs/StreamPETR/stream_petr_r50_flash_704_bs2_seq_428q_nui_60e.py)| [model](https://github.com/exiawsh/storage/releases/download/v1.0/stream_petr_r50_flash_704_bs2_seq_428q_nui_60e.pth)/[log](https://github.com/exiawsh/storage/releases/download/v1.0/stream_petr_r50_flash_704_bs2_seq_428q_nui_60e.log) | The detailed results can be found in the training log. For other results on nuScenes val set, please see [Here](docs/training_inference.md). **Notes**: - FPS is measured on NVIDIA RTX 3090 GPU with batch size of 1 (containing 6 view images, without using flash attention) and FP32. - The training time is measured with 8x 2080ti GPUs. - RepDETR3D uses deformable attention, which is inspired by DETR3D and Sparse4D. ## Results on NuScenes Test Set. | Model | Setting |Pretrain|NDS| mAP|AMOTA|AMOTP| | :---: | :---: | :---: | :---: | :---:| :---: | :---:| |StreamPETR| V2-99 - 900q | [DD3D](https://github.com/exiawsh/storage/releases/download/v1.0/dd3d_det_final.pth) | 63.6| 55.0 | - | - | |StreamPETR| ViT-Large-900q | - | 67.6| 62.0 | 65.3| 87.6 | ## Currently Supported Features - [x] StreamPETR code (also including PETR and Focal-PETR) - [x] Flash attention - [x] Deformable attention (RepDETR3D) - [x] Checkpoints - [x] Sliding window training - [x] Efficient training in streaming video - [ ] TensorRT inference - [x] 3D object tracking ## Acknowledgements We thank these great works and open-source codebases: * 3D Detection. [MMDetection3d](https://github.com/open-mmlab/mmdetection3d), [DETR3D](https://github.com/WangYueFt/detr3d), [PETR](https://github.com/megvii-research/PETR), [BEVFormer](https://github.com/fundamentalvision/BEVFormer), [SOLOFusion](https://github.com/Divadi/SOLOFusion), [Sparse4D](https://github.com/linxuewu/Sparse4D). * Multi-object tracking. [MOTR](https://github.com/megvii-research/MOTR), [PF-Track](https://github.com/TRI-ML/PF-Track). ## Citation If you find StreamPETR is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry. ```bibtex @article{wang2023exploring, title={Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection}, author={Wang, Shihao and Liu, Yingfei and Wang, Tiancai and Li, Ying and Zhang, Xiangyu}, journal={arXiv preprint arXiv:2303.11926}, year={2023} } ```