Before revision:  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
After revision:  including one-stage and two-stage detectors, as well as anchor-based and 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, 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