This tutorial collects answers to any How to xxx with MMDetection. Feel free to update this doc if you meet new questions about How to and find the answers!

# Use backbone network through MMClassification¶

The model registry in MMDet, MMCls, MMSeg all inherit from the root registry in MMEngine. This allows these repositories to directly use the modules already implemented by each other. Therefore, users can use backbone networks from MMClassification in MMDetection without implementing a network that already exists in MMClassification.

## Use backbone network implemented in MMClassification¶

Suppose you want to use MobileNetV3-small as the backbone network of RetinaNet, the example config is as the following.

_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
model = dict(
backbone=dict(
_delete_=True, # Delete the backbone field in _base_
type='mmcls.MobileNetV3', # Using MobileNetV3 from mmcls
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 MMCls 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))


## Use backbone network in TIMM through MMClassification¶

MMClassification also provides a wrapper for the PyTorch Image Models (timm) backbone network, users can directly use the backbone network in timm through MMClassification. Suppose you want to use EfficientNet-B1 as the backbone network of RetinaNet, the example config is as the following.

# https://github.com/open-mmlab/mmdetection/blob/dev-3.x/configs/timm_example/retinanet_timm-efficientnet-b1_fpn_1x_coco.py

_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
model = dict(
backbone=dict(
_delete_=True, # Delete the backbone field in _base_
type='mmcls.TIMMBackbone', # Using timm from mmcls
model_name='efficientnet_b1',
features_only=True,
pretrained=True,
out_indices=(1, 2, 3, 4)), # Modify out_indices
neck=dict(in_channels=[24, 40, 112, 320])) # Modify in_channels

optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)


type='mmcls.TIMMBackbone' means use the TIMMBackbone class from MMClassification in MMDetection, and the model used is EfficientNet-B1, where mmcls means the MMClassification repo and TIMMBackbone means the TIMMBackbone wrapper implemented in MMClassification.

For the principle of the Hierarchy Registry, please refer to the MMEngine document. For how to use other backbones in MMClassification, you can refer to the MMClassification document.

# Use Mosaic augmentation¶

If you want to use Mosaic in training, please make sure that you use MultiImageMixDataset at the same time. Taking the ‘Faster R-CNN’ algorithm as an example, you should modify the values of train_pipeline and train_dataset in the config as below:

# Open configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py directly and add the following fields
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
img_scale=(1333, 800)

train_pipeline = [
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-img_scale[0] // 2, -img_scale[1] // 2)), # The image will be enlarged by 4 times after Mosaic processing,so we use affine transformation to restore the image size.
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]

train_dataset = dict(
_delete_ = True, # remove unnecessary Settings
type='MultiImageMixDataset',
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=[
],
filter_empty_gt=False,
),
pipeline=train_pipeline
)

data = dict(
train=train_dataset
)


# Unfreeze backbone network after freezing the backbone in the config¶

If you have freezed the backbone network in the config and want to unfreeze it after some epoches, you can write a hook function to do it. Taking the Faster R-CNN with the resnet backbone as an example, you can freeze one stage of the backbone network and add a custom_hooks in the config as below:

_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
# freeze one stage of the backbone network.
backbone=dict(frozen_stages=1),
)
custom_hooks = [dict(type="UnfreezeBackboneEpochBasedHook", unfreeze_epoch=1)]


Meanwhile write the hook class UnfreezeBackboneEpochBasedHook in mmdet/core/hook/unfreeze_backbone_epoch_based_hook.py

from mmengine.model import is_model_wrapper
from mmengine.hooks import Hook
from mmdet.registry import HOOKS

@HOOKS.register_module()
class UnfreezeBackboneEpochBasedHook(Hook):
"""Unfreeze backbone network Hook.

Args:
unfreeze_epoch (int): The epoch unfreezing the backbone network.
"""

def __init__(self, unfreeze_epoch=1):
self.unfreeze_epoch = unfreeze_epoch

def before_train_epoch(self, runner):
# Unfreeze the backbone network.
# Only valid for resnet.
if runner.epoch == self.unfreeze_epoch:
model = runner.model
if is_model_wrapper(model):
model = model.module
backbone = model.backbone
if backbone.frozen_stages >= 0:
if backbone.deep_stem:
backbone.stem.train()
for param in backbone.stem.parameters():
else:
backbone.norm1.train()
for m in [backbone.conv1, backbone.norm1]:
for param in m.parameters():

for i in range(1, backbone.frozen_stages + 1):
m = getattr(backbone, f'layer{i}')
m.train()
for param in m.parameters():


# Get the channels of a new backbone¶

If you want to get the channels of a new backbone, you can build this backbone alone and input a pseudo image to get each stage output.

Take ResNet as an example:

from mmdet.models import ResNet
import torch
self = ResNet(depth=18)
self.eval()
inputs = torch.rand(1, 3, 32, 32)
level_outputs = self.forward(inputs)
for level_out in level_outputs:
print(tuple(level_out.shape))



Output of the above script is as below:

(1, 64, 8, 8)
(1, 128, 4, 4)
(1, 256, 2, 2)
(1, 512, 1, 1)


Users can get the channels of the new backbone by Replacing the ResNet(depth=18) in this script with their customized backbone.

# Use Detectron2 Model in MMDetection¶

Users can use Detectron2Wrapper to run Detectron2’s model in MMDetection. We provide examples of Faster R-CNN, Mask R-CNN, and RetinaNet in MMDetection.

The algorithm components in config file should be the same as those of in Detectron2. During setup, we will first initialize the default settings, which can be found in Detectron2. Then, the settings in config file will overwrite the default settings and the model will be built with these settings. The input data will first convert to Detectron2’s type and feed into Detectron2’s model. During inference the results calculate from Detectron2’s model will reconvert back to the MMDetection’s type.

## Use Detectron2’s pre-trained weights¶

The weight initialization in Detectron2Wrapper will not use the logic of MMDetection. Users can set model.d2_detector.weights=xxx to load pre-trained weights. For example, we can use model.d2_detector.weights='detectron2://ImageNetPretrained/MSRA/R-50.pkl' to load the pre-trained ResNet-50 or use model.d2_detector.weights='detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl' to load the pre-trained Mask R-CNN weights proposed in Detectron2.

Note: Detectron2’s pretrained model cannot be loaded directly by using load_from, it should be first converted via tools/model_converters/detectron2_to_mmdet.py

For inference of released detectron2 checkpoints, users should first use tools/model_converters/detectron2_to_mmdet.py to convert Detectron2 checkpoint to MMDetection.

python tools/model_converters/detectron2_to_mmdet.py ${Detectron2 ckpt path}${MMDetectron ckpt path}