我使用的是vmamba_tiny_s1l8
def vmamba_tiny_s1l8(channel_first=True): return VSSM( depths=[2, 2, 8, 2], dims=96, drop_path_rate=0.2, patch_size=4, in_chans=3, num_classes=1000, ssm_d_state=1, ssm_ratio=1.0, ssm_dt_rank="auto", ssm_act_layer="silu", ssm_conv=3, ssm_conv_bias=False, ssm_drop_rate=0.0, ssm_init="v0", forward_type="v05_noz", mlp_ratio=4.0, mlp_act_layer="gelu", mlp_drop_rate=0.0, gmlp=False, patch_norm=True, norm_layer=("ln2d" if channel_first else "ln"), downsample_version="v3", patchembed_version="v2", use_checkpoint=False, posembed=False, imgsize=224, )加载的权重文件是在COCO数据集上面的目标检测mask_rcnn_vssm_fpn_coco_tiny_ms_3x_s_epoch_31.pth，但是加载后会出现很多参数缺失和不必要参数的情况。
我使用的是vmamba_tiny_s1l8
def vmamba_tiny_s1l8(channel_first=True): return VSSM( depths=[2, 2, 8, 2], dims=96, drop_path_rate=0.2, patch_size=4, in_chans=3, num_classes=1000, ssm_d_state=1, ssm_ratio=1.0, ssm_dt_rank="auto", ssm_act_layer="silu", ssm_conv=3, ssm_conv_bias=False, ssm_drop_rate=0.0, ssm_init="v0", forward_type="v05_noz", mlp_ratio=4.0, mlp_act_layer="gelu", mlp_drop_rate=0.0, gmlp=False, patch_norm=True, norm_layer=("ln2d" if channel_first else "ln"), downsample_version="v3", patchembed_version="v2", use_checkpoint=False, posembed=False, imgsize=224, )加载的权重文件是在COCO数据集上面的目标检测mask_rcnn_vssm_fpn_coco_tiny_ms_3x_s_epoch_31.pth，但是加载后会出现很多参数缺失和不必要参数的情况。