MapGR: MapQR + MapGR checkpoint (nuScenes, ResNet-50, 24 epochs)

This repository hosts the released weights for Learning Global Representation from Queries for Vectorized HD Map Construction (ICML 2026, arXiv:2510.06969, project page).

Training and evaluation code, installation and data preparation are in the GitHub repository XR-Lee/MapGR (mapqr/ sub-project).

File	Description
mapqr_mapgr_nusc_r50_24ep.pth	Model weights: state_dict plus minimal meta, no optimizer state.
mapqr_mapgr_nusc_r50_24ep_config.py	Fully resolved config, for reference. Train and evaluate with mapqr/projects/configs/mapgr/mapqr_mapgr_nusc_r50_24ep.py from the code repository.

Results (nuScenes val)

	mAP1	mAP2	APdiv / APped / APbou (mAP2 thresholds)
Paper (MapQR + MapGR, 24 ep)	45.3	68.1	69.8 / 65.8 / 68.8
This checkpoint	44.87	67.96	69.35 / 64.95 / 69.57

mAP₂ averages Chamfer AP over the {0.5, 1.0, 1.5} m thresholds; mAP₁ averages over {0.2, 0.5, 1.0} m.

This checkpoint is not the run reported in the paper.

• It was trained with the research code the GitHub repository was cleaned up from, using the same settings as the released config.
• Training: 8 GPUs × 4 samples, fp16.
• The numbers above come from evaluating this exact file with the released tools/test.py.
• Other 24-epoch runs of closely related configurations (different seeds, loss weights and query counts) reached 67.2–68.0 mAP₂.

# inside mapqr/ of the code repository
wget -P ckpts https://huggingface.co/Lee1413/MapGR/resolve/main/mapqr_mapgr_nusc_r50_24ep.pth
./tools/dist_test_map.sh projects/configs/mapgr/mapqr_mapgr_nusc_r50_24ep.py ckpts/mapqr_mapgr_nusc_r50_24ep.pth 1

SHA-256 of mapqr_mapgr_nusc_r50_24ep.pth: d4f868312234806b5c0a97ea2c7edf71173de3fac1d510742a99317e1c6f6916

Training logs

mmdet3d logs of the two 24-epoch nuScenes runs. User names, host names and internal path prefixes are replaced with <user>, <host> and <workspace>. Nothing else is changed.

Run	Files	Setup	mAP2 at epoch 12 / 24
MapQR + MapGR (this checkpoint)	20260329_014512.log, .log.json	8 GPUs × 4 samples, fp16, PyTorch 2.0.1	59.90 / 67.96
MapTRv2 + MapGR (the paper's run; weights not released)	train.273710.log	4 GPUs × 4 samples, fp16, PyTorch 1.9.1	56.14 / 64.98

Citation

@inproceedings{qiu2026mapgr,
  title     = {Learning Global Representation from Queries for Vectorized {HD} Map Construction},
  author    = {Qiu, Shoumeng and Li, Xinrun and Long, Yang and Xue, Xiangyang and Ojha, Varun and Pu, Jian},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}