ban_vit-l14-clip_mit-b0_256x256_100e_whucd.py

_base_ = ['./ban_vit-b16-clip_mit-b0_256x256_100e_whucd.py']

pretrained = 'pretrain/clip_vit-large-patch14-336_3rdparty-0b5df9cb.pth'  # noqa

model = dict(
    type='BAN',
    pretrained=pretrained,
    encoder_resolution=dict(
        size=(336, 336),
        mode='bilinear'),
    image_encoder=dict(
        type='mmseg.VisionTransformer',
        img_size=(336, 336),
        patch_size=14,
        patch_pad=0,
        embed_dims=1024,
        num_layers=18,
        num_heads=16,
        out_indices=(5, 11, 17)),
    decode_head=dict(
        type='BitemporalAdapterHead',
        ban_cfg=dict(
            fusion_index=[1, 2, 3],
            clip_channels=1024),
        ban_dec_cfg=dict(
            in_channels=[32, 64, 160, 256])))

train_dataloader = dict(batch_size=8, num_workers=8)
val_dataloader = dict(batch_size=1, num_workers=1)