Table 1: Supported features of different codebases. “X” means officially supported, “*” means supported in a forked reposi-
tory and blank means not supported.
MMDetection maskrcnn-benchmark Detectron SimpleDet
Fast R-CNN X X X X
Faster R-CNN X X X X
Mask R-CNN X X X X
RetinaNet X X X X
DCN X X X X
DCNv2 X X
Mixed Precision Training X X X
Cascade R-CNN X * X
Weight Standardization X *
Mask Scoring R-CNN X *
FCOS X *
SSD X
R-FCN X
M2Det X
GHM X
ScratchDet X
Double-Head R-CNN X
Grid R-CNN X
FSAF X
Hybrid Task Cascade X
Guided Anchoring X
Libra R-CNN X
Generalized Attention X
GCNet X
HRNet X
TridentNet [17] X
3. Architecture
3.1. Model Representation
Although the model architectures of different detectors
are different, they have common components, which can be
roughly summarized into the following classes.
Backbone Backbone is the part that transforms an image
to feature maps, such as a ResNet-50 without the last fully
connected layer.
Neck Neck is the part that connects the backbone and heads.
It performs some refinements or reconfigurations on the raw
feature maps produced by the backbone. An example is
Feature Pyramid Network (FPN).
DenseHead (AnchorHead/AnchorFreeHead) DenseHead
is the part that operates on dense locations of feature maps,
including AnchorHead and AnchorFreeHead, e.g., RPN-
Head, RetinaHead, FCOSHead.
RoIExtractor RoIExtractor is the part that extracts RoI-
wise features from a single or multiple feature maps with
RoIPooling-like operators. An example that extracts RoI
features from the corresponding level of feature pyramids is
SingleRoIExtractor.
RoIHead (BBoxHead/MaskHead) RoIHead is the part
that takes RoI features as input and make RoI-wise task-
specific predictions, such as bounding box classifica-
tion/regression, mask prediction.
With the above abstractions, the framework of single-
stage and two-stage detectors is illustrated in Figure 1. We
can develop our own methods by simply creating some new
components and assembling existing ones.
3.2. Training Pipeline
We design a unified training pipeline with hooking
mechanism. This training pipeline can not only be used for
object detection, but also other computer vision tasks such
as image classification and semantic segmentation.
The training processes of many tasks share a similar
workflow, where training epochs and validation epochs
run iteratively and validation epochs are optional. In
each epoch, we forward and backward the model by
many iterations. To make the pipeline more flexible
and easy to customize, we define a minimum pipeline
3
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