<div id="top" align="center">

# InternImage for CVPR 2023 Workshop on End-to-End Autonomous Driving

</div>

## 1. InternImage-based Baseline for CVPR23 Occupancy Prediction Challenge

We achieve an improvement of 1.44 in MIOU baseline by leveraging the InterImage-based model.

| model name             |                                                weight                                                 | mIoU  | others | barrier | bicycle |  bus  |  car  | construction_vehicle | motorcycle | pedestrian | traffic_cone | trailer | truck | driveable_surface | other_flat | sidewalk | terrain | manmade | vegetation |
| ---------------------- | :---------------------------------------------------------------------------------------------------: | :---: | :----: | :-----: | :-----: | :---: | :---: | :------------------: | :--------: | :--------: | :----------: | :-----: | :---: | :---------------: | :--------: | :------: | :-----: | :-----: | :--------: |
| bevformer_intern-s_occ |  [Google Drive](https://drive.google.com/file/d/1LV9K8hrskKf51xY1wbqTKzK7WZmVXEV_/view?usp=sharing)   | 25.11 |  6.93  |  35.57  |  10.40  | 35.97 | 41.23 |        13.72         |   20.30    |   21.10    |    18.34     |  19.18  | 28.64 |       49.82       |   30.74    |  31.00   |  27.44  |  19.29  |   17.29    |
| bevformer_base_occ     | [Google Drive](https://drive.google.com/file/d/1NyoiosafAmne1qiABeNOPXR-P-y0i7_I/view?usp=share_link) | 23.67 |  5.03  |  38.79  |  9.98   | 34.41 | 41.09 |        13.24         |   16.50    |   18.15    |    17.83     |  18.66  | 27.70 |       48.95       |   27.73    |  29.08   |  25.38  |  15.41  |   14.46    |

### Get Started

please refer to [README.md](./occupancy_prediction/README.md)

## 2. InternImage-based Baseline for Online HD Map Construction Challenge For Autonomous Driving

By incorporating the InterImage-based model, we observe an enhancement of 6.56 in mAP baseline.

| model name          |                                                      weight                                                       | $\\mathrm{mAP}$ | $\\mathrm{AP}\_{pc}$ | $\\mathrm{AP}\_{div}$ | $\\mathrm{AP}\_{bound}$ |
| ------------------- | :---------------------------------------------------------------------------------------------------------------: | :-------------: | :------------------: | :-------------------: | :---------------------: |
| vectormapnet_intern | [Checkpoint](https://github.com/OpenGVLab/InternImage/releases/download/track_model/vectormapnet_internimage.pth) |      49.35      |        45.05         |         56.78         |          46.22          |
| vectormapnet_base   |              [Google Drive](https://drive.google.com/file/d/16D1CMinwA8PG1sd9PV9_WtHzcBohvO-D/view)               |      42.79      |        37.22         |         50.47         |          40.68          |

### Get Started

please refer to [README.md](Online-HD-Map-Construction/README.md)

## 3. InternImage-based Baseline for CVPR23 OpenLane-V2 Challenge

Through the implementation of the InterImage-based model, we achieve an advancement of 0.009 in F-score baseline.

|             | OpenLane-V2 Score | DET<sub>l</sub> | DET<sub>t</sub> | TOP<sub>ll</sub> | TOP<sub>lt</sub> | F-Score |
| ----------- | ----------------- | --------------- | --------------- | ---------------- | ---------------- | ------- |
| base r50    | 0.292             | 0.183           | 0.457           | 0.022            | 0.143            | 0.215   |
| InternImage | 0.325             | 0.194           | 0.537           | 0.02             | 0.17             | 0.224   |

### Get Started

please refer to [README.md](./openlane-v2/README.md)