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
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
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Consistent
Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent
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backbone network
and flexible backbone framework
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by constructing compositions of
to better train
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under the pre-training fine-tuning protocol
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CBNetV2
it
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the Assistant Supervision
assistant supervision
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CBNetV2 has strong generalization capabilities for different backbones and head designs of the detector architecture.
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adapt
adapted
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the head designs of
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performance
performances
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with
under the
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, our HTC Dual-Swin-B achieves 58.6
protocol, our Dual-Swin-L achieves 59.4
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51.1
51.6
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achieved by HTC++ with Swin-L
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released
available