About differences between configs on nuScenes and Argoverse2

[curiosity654]

Hi, thank you for your great work. As the FSF model on AV2 isn't released, I'm trying to train on AV2 myself. I found that the pretrained FSD model on nuScenes and AV2 are very different in terms of size (900+MB on nuScenes and 100+MB on AV2). Looking into the configs I found that the Sparse UNet backbone setting are very different, with nuScenes's backbone channel number are generally 2 times larger than on AV2. Is there any insight behind the design or just tuned by experience? Thank you very much for any feedback.

config of AV2:

backbone=dict(
        type='SimpleSparseUNet',
        in_channels=64,
        sparse_shape=[32, 2048, 2048],
        order=('conv', 'norm', 'act'),
        norm_cfg=dict(type='naiveSyncBN1d', eps=1e-3, momentum=0.01),
        base_channels=64,
        output_channels=128,
        encoder_channels=((64, ), (64, 64, 64), (64, 64, 64), (128, 128, 128)),
        encoder_paddings=((1, ), (1, 1, 1), (1, 1, 1), ((0, 1, 1), 1, 1)),
        decoder_channels=((128, 128, 64), (64, 64, 64), (64, 64, 64), (64, 64, 64)),
        decoder_paddings=((1, 0), (1, 0), (0, 0), (0, 1)),
    ),

config of nuScenes:

backbone=dict(
        type='SimpleSparseUNet',
        in_channels=64,
        sparse_shape=sparse_shape,
        order=('conv', 'norm', 'act'),
        norm_cfg=dict(type='naiveSyncBN1d', eps=1e-3, momentum=0.01),
        base_channels=64,
        output_channels=128, 
        encoder_channels=((128, ), (128, 128, 128), (128, 128, 128), (256, 256, 256), (512, 512, 512)),
        encoder_paddings=((1, ), (1, 1, 1), (1, 1, 1), ((0, 1, 1), 1, 1), (1, 1, 1)),
        decoder_channels=((512, 512, 256), (256, 256, 128), (128, 128, 128), (128, 128, 128), (128, 128, 128)),
        decoder_paddings=((1, 1), (1, 0), (1, 0), (0, 0), (0, 1)), 
    ),