Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. However, designing or searching for a new backbone and pre-training it on ImageNet may require a large number of computational resources, making it costly to obtain better detection performance. In this paper, we propose a novel backbone network, namely CBNetV2, by constructing compositions of existing open-sourced pre-trained backbones. In particular, CBNetV2 architecture groups multiple identical backbones, which are connected through composite connections.  We also propose a better training strategy with the Assistant Supervision for CBNet-based detectors. Without additional pre-training, CBNetV2 can be integrated into mainstream detectors, including one-stage and two-stage detectors , as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO. Also, experiments provide strong evidence showing that composite backbones are more efficient  and resource-friendly than pre-trained wider and deeper networks  , including manual-based and NAS-based, as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with a larger backbone Swin-L. Code will be released at URL
Consistent performance gains through exploring more effective network structures. In this paper, we propose a novel backbone network, namely CBNetV2, by constructing compositions of existing open-sourced pre-trained backbones. In particular, CBNetV2 architecture groups multiple identical backbones, which are connected through composite connections. Specifically, CBNetV2 integrates the high- and low-level features of multiple backbone networks and gradually expands the receptive field to more efficiently perform object detection. We also propose a better training strategy with the Assistant Supervision for CBNet-based detectors. Without additional pre-training, CBNetV2 can be adapt to various backbones, including manual-based and NAS-based , as well as CNN-based and Transformer-based ones. Experiments provide strong evidence showing that composite backbones are more efficient , effective, and resource-friendly than wider and deeper networks . CBNetV2 is compatible with most mainstream detectors, including one-stage and two-stage detectors , as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L. Code is released at URL
R
Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent
Consistent
D
However, designing or searching for a new backbone and pre-training it on ImageNet may require a large number of computational resources, making it costly to obtain better detection performance.
A
Specifically, CBNetV2 integrates the high- and low-level features of multiple backbone networks and gradually expands the receptive field to more efficiently perform object detection.
R
integrated into mainstream detectors, including one-stage and two-stage detectors
adapt to various backbones, including manual-based and NAS-based
R
anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO. Also, experiments
CNN-based and Transformer-based ones. Experiments
A
, effective,
D
pre-trained
A
. CBNetV2 is compatible with most mainstream detectors, including one-stage and two-stage detectors
D
including manual-based and NAS-based,
R
CNN-based and Transformer-based ones
anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO
D
a stronger baseline
D
a larger backbone
R
will be
is