# Object Detection on Argoverse 2 (val)

Source: [Object Detection on Argoverse 2 (val)](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-31T10:43:34.889Z

Evaluation of Object Detection performance on the Argoverse 2 validation split, focusing on tracking error metrics (FN/FP) and effort-based performance indicators (MDR, FSR, LEA).

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Object Detection |
| Dataset (including any stated split) | Argoverse 2 (val) |
| Metric | FN Count |
| Unit | SCALAR |
| Direction | Lower is better |
| Results loaded | 6 of 6 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [FN Count](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=FN%20Count>): Lower is better; unit: SCALAR.

- [FP Count](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=FP%20Count>): Lower is better; unit: SCALAR.

- [Critical FN Count](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Critical%20FN%20Count>): Lower is better; unit: SCALAR.

- [Critical FP Count](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Critical%20FP%20Count>): Lower is better; unit: SCALAR.

- [TC Count](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=TC%20Count>): Higher is better; unit: SCALAR.

- [Mean Effort MDR](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Mean%20Effort%20MDR>): Lower is better; unit: SCALAR.

- [Mean Effort FSR](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Mean%20Effort%20FSR>): Lower is better; unit: PERCENT.

- [Mean Effort LEA](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Mean%20Effort%20LEA>): Lower is better; unit: SCALAR.

- [Cumulative Effort MDR](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Cumulative%20Effort%20MDR>): Lower is better; unit: SCALAR.

- [Cumulative Effort FSR](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Cumulative%20Effort%20FSR>): Lower is better; unit: SCALAR.

- [Cumulative Effort LEA](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Cumulative%20Effort%20LEA>): Lower is better; unit: SCALAR.

- [Worst Case MDR](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Worst%20Case%20MDR>): Lower is better; unit: SCALAR.

- [Worst Case FSR](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Worst%20Case%20FSR>): Lower is better; unit: SCALAR.

- [Worst Case LEA](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Worst%20Case%20LEA>): Lower is better; unit: SCALAR.

- [Precision](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Precision>): Higher is better; unit: PERCENT.

- [Recall](<https://www.sota2.com/research/sota/object-detection-on-argoverse-2-val.md?metric=Recall>): Higher is better; unit: PERCENT.

## Recorded results

| Method | FN Count | Result date | Report paper |
| --- | --- | --- | --- |
| CenterPoint | 548 | Not recorded | 69cb553b0636e5bf4afca0f9 |
| AB3DMOT | 560 | Not recorded | 69cb553b0636e5bf4afca0f9 |
| BEVFusion | 597 | Not recorded | 69cb553b0636e5bf4afca0f9 |
| CenterPoint | 8162 | Not recorded | 69cb553b0636e5bf4afca0f9 |
| BEVFusion | 8617 | Not recorded | 69cb553b0636e5bf4afca0f9 |
| AB3DMOT | 9074 | Not recorded | 69cb553b0636e5bf4afca0f9 |

### CenterPoint

FN Count: 548. Result ID: 69cba54347f34ce924b4a7c0.

[Reporting paper](<https://www.sota2.com/research/paper/69cb553b0636e5bf4afca0f9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6af91e209131cea2b72c.md>)

[Implementation](<https://github.com/tianweiy/CenterPoint>)

Recorded evaluation configuration: {"Pipeline":"CenterPoint","Modality":"LiDAR","Class":"Truck"}. This record does not assert independent reproduction.

### AB3DMOT

FN Count: 560. Result ID: 69cba54347f34ce924b4a7be.

[Reporting paper](<https://www.sota2.com/research/paper/69cb553b0636e5bf4afca0f9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa68091e209131cea19b42.md>)

[Implementation](<https://github.com/xinshuoweng/AB3DMOT>)

Recorded evaluation configuration: {"Pipeline":"AB3DMOT","Modality":"LiDAR","Class":"Truck"}. This record does not assert independent reproduction.

### BEVFusion

FN Count: 597. Result ID: 69cba54347f34ce924b4a7c2.

[Reporting paper](<https://www.sota2.com/research/paper/69cb553b0636e5bf4afca0f9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa74b01e209131cea63405.md>)

[Implementation](<https://github.com/mit-han-lab/bevfusion>)

Recorded evaluation configuration: {"Pipeline":"BEVFusion","Modality":"LiDAR + Camera","Class":"Truck"}. This record does not assert independent reproduction.

### CenterPoint

FN Count: 8162. Result ID: 69cba54347f34ce924b4a7bf.

[Reporting paper](<https://www.sota2.com/research/paper/69cb553b0636e5bf4afca0f9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6af91e209131cea2b72c.md>)

[Implementation](<https://github.com/tianweiy/CenterPoint>)

Recorded evaluation configuration: {"Pipeline":"CenterPoint","Modality":"LiDAR","Class":"Car"}. This record does not assert independent reproduction.

### BEVFusion

FN Count: 8617. Result ID: 69cba54347f34ce924b4a7c1.

[Reporting paper](<https://www.sota2.com/research/paper/69cb553b0636e5bf4afca0f9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa74b01e209131cea63405.md>)

[Implementation](<https://github.com/mit-han-lab/bevfusion>)

Recorded evaluation configuration: {"Pipeline":"BEVFusion","Modality":"LiDAR + Camera","Class":"Car"}. This record does not assert independent reproduction.

### AB3DMOT

FN Count: 9074. Result ID: 69cba54347f34ce924b4a7bd.

[Reporting paper](<https://www.sota2.com/research/paper/69cb553b0636e5bf4afca0f9.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa68091e209131cea19b42.md>)

[Implementation](<https://github.com/xinshuoweng/AB3DMOT>)

Recorded evaluation configuration: {"Pipeline":"AB3DMOT","Modality":"LiDAR","Class":"Car"}. This record does not assert independent reproduction.

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