I want to train the pointpillar_v2vnet model on both OpV2V and V2XSet dataset, the yaml file are the same except for the 'root_dir' and 'validate_dir'. One is 'opv2v/train', 'opv2v/validate' and the other one is 'v2xset/train', 'v2xset/validate'. For the OPV2V dataset, AP@0.5=0.914, AP@0.7=0.798, but for the V2XSet dataset, AP@0.5=0.672, AP@0.7=0.315
Why does the performance differ so much between two datasets? Is it normal?
V2XSet is usually more challenging than opv2v, but from my own experiments, the difference is not that large as yours. I think you didn't train the models well on V2XSet
I want to train the pointpillar_v2vnet model on both OpV2V and V2XSet dataset, the yaml file are the same except for the 'root_dir' and 'validate_dir'. One is 'opv2v/train', 'opv2v/validate' and the other one is 'v2xset/train', 'v2xset/validate'. For the OPV2V dataset, AP@0.5=0.914, AP@0.7=0.798, but for the V2XSet dataset, AP@0.5=0.672, AP@0.7=0.315 Why does the performance differ so much between two datasets? Is it normal?