Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to better train existing open-sourced pre-trained backbones under the pre-training fine-tuning protocol . In particular, CBNetV2 architecture groups multiple identical backbones, which are connected through composite connections. Specifically, it 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 assistant supervision for CBNet-based detectors. CBNetV2 has strong generalization capabilities for different backbones and head designs of the detector architecture. Without additional pre-training  , CBNetV2 can be adapted 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 the head designs of most mainstream detectors , including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% 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)  . Code is available at URL
Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to construct high-performance detectors using existing open-sourced pre-trained backbones under the pre-training fine-tuning paradigm . In particular, CBNetV2 architecture groups multiple identical backbones, which are connected through composite connections. Specifically, it 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 assistant supervision for CBNet-based detectors. Without additional pre-training of the composite backbone , CBNetV2 can be adapted to various backbones ( CNN-based vs. Transformer-based ) and head designs of most mainstream detectors ( one-stage vs. two-stage , anchor-based vs. anchor-free-based ). Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNetV2 introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks . Particularly, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev under the single-model and single-scale testing protocol , which is significantly better than the state-of-the-art result ( 57.7\% box AP and 50.2\% mask AP) achieved by Swin-L, while the training schedule is reduced by 6\times. With multi-scale testing, we push the current best single model result to a new record of 60.1\% box AP and 52.3\% mask AP without using extra training data . Code is available at URL
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