[CVPR2024] Traffic Scene Parsing through the TSP6K Dataset

The dataset and code in TSP6K dataset. Code is implemented using an open-source semantic segmentation toolbox, MMsegmentation.

Installation

Please follow the installation instructions in mmsegmentation. In our environment, we use the following versions of different packages. mmsegmentation==0.20.2 mmcv-full=1.4.0

Install the mmseg lib first git clone https://github.com/PengtaoJiang/TSP6K.git cd TSP6K/ pip install -v -e . If you want to evaluate the iIoU score, please install the cityscapesscript lib cd mmseg/datasets/cityscapesscripts/ python setup.py build install

Dataset Preparation

Download the dataset from this link(Google Drive) or this link(jianguoyun)(password: Wi9qFT) or this link(baidu disk)(password: jzra) and put them into /data/TSP6K/. data ├── TSP6K │ ├── image │ ├── label │ ├── split You can also download the COCO-style instance bounding box annotations from this link.

Training

Train SegNext with the proposed Detail Refining Decoder using the following command bash tools/dist_train.sh \ configs/tsp6k/segnext_base_1024x1024_160k_tsp6k_msaspp_rrm_5tokens_12heads.py \ 8 --auto-resume

Evaluation

Results and models

| Method | Backbone | Crop Size |Lr Sche. | val mIoU (ms) | val iIoU (ms) | config | model | | :----- |:-----: |:-----: |:---: |:---: |:---: |:---: |:---: | | SegNext+DRD | MSCAN-B | 1024x1024 | 160000 | 75.8 | 58.4 | config | model | | SegNext+DRD | MSCAN-L | 1024x1024 | 160000 | 76.2 | 58.9 | config | model |

We provide the pre-trained segmentation models above. You can download them and directly evaluate them by bash tools/dist_test.sh \ configs/tsp6k/segnext_base_1024x1024_160k_tsp6k_msaspp_rrm_5tokens_12heads.py \ ./work_dirs/segnext_base_1024x1024_160k_tsp6k_msaspp_rrm_5tokens_12heads/latest.pth \ 8 --out ./work_dirs/segnext_base_1024x1024_160k_tsp6k_msaspp_rrm_5tokens_12heads/results.pkl \ --aug-test --eval mIoU Evaluate the segmentation model using the iIoU metric by bash tools/dist_test.sh \ configs/tsp6k/segnext_base_1024x1024_160k_tsp6k_msaspp_rrm_5tokens_12heads.py \ ./work_dirs/segnext_base_1024x1024_160k_tsp6k_msaspp_rrm_5tokens_12heads/latest.pth \ 8 --out ./work_dirs/segnext_base_1024x1024_160k_tsp6k_msaspp_rrm_5tokens_12heads/results.pkl \ --aug-test --eval cityscapes

Citation

If you find the proposed TSP6K dataset and segmentation network are useful for your research, please cite @inproceedings{jiang2024traffic, title={Traffic Scene Parsing through the TSP6K Dataset}, author={Jiang, Peng-Tao and Yang, Yuqi and Cao, Yang and Hou, Qibin and Cheng, Ming-Ming and Shen, Chunhua}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, year={2024} }