training_inference.md

Training and Evaluation

Training

If you want to train the model, please run the following command:

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

For example, if you want to train TopoMLP on OpenLane-V2 subset-A train set, please run the following command:

./tools/dist_train.sh projects/configs/topomlp/topomlp_setA_r50_wo_yolov8.py 8 --work-dir=./work_dirs/topomlp_setA_r50_wo_yolov8

The training on 8 Nvidia A100 GPUs takes about 15 hours.

Evaluation

If you want to evaluate the model, please run the following command:

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} --eval=bbox

Main Results

OpenLane-V2 subset-A val set:

Method	Backbone	Epoch	DETl	TOPll	DETt	TOPlt	OLS	Weight/Log
STSU	ResNet-50	24	12.7	0.5	43.0	15.1	25.4	-
VectorMapNet	ResNet-50	24	11.1	0.4	41.7	6.2	20.8	-
MapTR	ResNet-50	24	17.7	1.1	43.5	10.4	26.0	-
TopoNet	ResNet-50	24	28.5	4.1	48.1	20.8	35.6	-
TopoMLP	ResNet-50	24	28.5	7.1	49.5	23.4	38.3	weight/log
TopoMLP*	ResNet-50	24	28.8	7.8	53.3	30.1	41.2

$*$ means using YOLOv8 proposals.

OpenLane-V2 subset-B val set:

Method	Backbone	Epoch	DETl	TOPll	DETt	TOPlt	OLS	Weight/Log
STSU	ResNet-50	24	8.2	0.0	43.9	9.4	21.2	-
VectorMapNet	ResNet-50	24	3.5	0.0	49.1	1.4	16.3	-
MapTR	ResNet-50	24	15.2	0.5	54.0	6.1	25.2	-
TopoNet	ResNet-50	24	24.3	2.5	55.0	14.2	33.2	-
TopoMLP	ResNet-50	24	26.6	7.6	58.3	17.8	38.7	weight/log