MMdetection之使用mmpretrain实现的骨干网络模型在mmdetection中（重要）

通过mmpretrain使用骨干网

MMDet、MMPreTrain、MMSeg中的模型注册表都继承自MMEngine中的根注册表。这允许这些存储库直接使用彼此已经实现的模块。因此，用户可以在MMDetection中使用MMPretrain中的骨干网，而无需实现MMPretrain中已经存在的网络。

Use backbone network implemented in MMPretrain

假设您希望使用MobileNetV3-small作为RetinaNet的骨干网，配置示例如下：

_base_ = [
    '../_base_/models/retinanet_r50_fpn.py',
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(imports=['mmpretrain.models'], allow_failed_imports=False)
pretrained = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth'
model = dict(
    backbone=dict(
        _delete_=True, # Delete the backbone field in _base_
        type='mmpretrain.MobileNetV3', # Using MobileNetV3 from mmpretrain
        arch='small',
        out_indices=(3, 8, 11), # Modify out_indices
        init_cfg=dict(
            type='Pretrained',
            checkpoint=pretrained,
            prefix='backbone.')), # The pre-trained weights of backbone network in mmpretrain have prefix='backbone.'. The prefix in the keys will be removed so that these weights can be normally loaded.
    # Modify in_channels
    neck=dict(in_channels=[24, 48, 96], start_level=0))

利用mmpretrain中的simmim自监督预训练模型在mmdetection中的mask-rcnn模型进行微调训练

How to use the mmpretrain model in mmdetection