nocs_yopo_real_camera_swinl.py

_base_ = './nocs_yopo_real_camera_r50.py'

load_from = 'https://github.com/RistoranteRist/mmlab-weights/releases/download/dino-swinl/dino-5scale_swin-l_8xb2-36e_coco-5486e051.pth'

num_levels = 5
model = dict(
    num_feature_levels=num_levels,
    backbone=dict(
        _delete_=True,
        type='SwinTransformer',
        pretrain_img_size=384,
        embed_dims=192,
        depths=[2, 2, 18, 2],
        num_heads=[6, 12, 24, 48],
        window_size=12,
        mlp_ratio=4,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.,
        attn_drop_rate=0.,
        drop_path_rate=0.2,
        patch_norm=True,
        out_indices=(0, 1, 2, 3),
        # Please only add indices that would be used
        # in FPN, otherwise some parameter will not be used
        with_cp=True),
    neck=dict(in_channels=[192, 384, 768, 1536], num_outs=num_levels),
    encoder=dict(layer_cfg=dict(self_attn_cfg=dict(num_levels=num_levels))),
    decoder=dict(layer_cfg=dict(cross_attn_cfg=dict(num_levels=num_levels))),
    bbox_head=dict(
        num_classes=6,
        classwise_rotation=True,
        classwise_sizes=True,
    ),
)

train_dataloader = dict(
    batch_size=8,
    num_workers=4)