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Update results on nuScenes and Waymo test set (#222)

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Haotian (Ken) Tang 2022-11-06 12:26:38 -05:00 committed by GitHub
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@ -23,12 +23,24 @@ Multi-sensor fusion is essential for an accurate and reliable autonomous driving
## Results
### 3D Object Detection (on Waymo test)
| Model | mAP-L1 | mAPH-L1 | mAP-L2 | mAPH-L2 |
| :-------: | :------: | :--: | :--: | :--: |
| [BEVFusion](https://waymo.com/open/challenges/entry/?challenge=DETECTION_3D&challengeId=DETECTION_3D&emailId=f58eed96-8bb3&timestamp=1658347965704580) | 82.72 | 81.35 | 77.65 | 76.33 |
| [BEVFusion-TTA](https://waymo.com/open/challenges/entry/?challenge=DETECTION_3D&challengeId=DETECTION_3D&emailId=94ddc185-d2ce&timestamp=1663562767759105) | 86.04 | 84.76 | 81.22 | 79.97 |
Here, BEVFusion only uses a single model without any test time augmentation. BEVFusion-TTA uses single model with test-time augmentation and no model ensembling is applied.
### 3D Object Detection (on nuScenes test)
| Model | Modality | mAP | NDS |
| :-------: | :------: | :--: | :--: |
| BEVFusion-e | C+L | 74.99 | 76.09 |
| BEVFusion | C+L | 70.23 | 72.88 |
| BEVFusion-base* | C+L | 71.72 | 73.83 |
*: We scaled up MACs of the model to match the computation cost of concurrent work.
### 3D Object Detection (on nuScenes validation)