CBNet: A Novel Composite Backbone Network Architecture for Object Detection
Authors: Yudong Liu, Yongtao Wang, Siwei Wang, Tingting Liang, Qijie Zhao, Zhi Tang, Haibin Ling11653-11660
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the widely tested MS-COCO benchmark (Lin et al. 2014), we conduct experiments by applying the proposed Composite Backbone Network to several state-of-the-art object detectors, such as FPN (Lin et al. 2017a), Mask RCNN (He et al. 2017) and Cascade R-CNN (Cai and Vasconcelos 2018). Experimental results show that the m APs of all the detectors consistently increase by 1.5 to 3.0 points, which demonstrates the effectiveness of our Composite Backbone Network. |
| Researcher Affiliation | Academia | Yudong Liu,1 Yongtao Wang,1 Siwei Wang,1 Tingting Liang,1 Qijie Zhao,1 Zhi Tang,1 Haibin Ling2 1Wangxuan Institute of Computer Technology, Peking University 2Department of Computer Science, Stony Brook University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be made available at https://github.com/PKUbahuangliuhe/CBNet. |
| Open Datasets | Yes | On the widely tested MS-COCO benchmark (Lin et al. 2014), we conduct experiments by applying the proposed Composite Backbone Network to several state-of-the-art object detectors |
| Dataset Splits | Yes | Following the protocol in MS-COCO, we use the trainval35k set for training, which is a union of 80k images from the train split and a random 35k subset of images from the 40k image validation split. |
| Hardware Specification | Yes | We conduct experiments on a machine with 4 NVIDIA Titan X GPUs, CUDA 9.2 and cu DNN 7.1.4 for most experiments. In addition, we train Cascade Mask R-CNN with Dual-Res Ne Xt152 on a machine with 4 NVIDIA P40 GPUs and Cascade Mask R-CNN with Triple Res Ne Xt152 on a machine with 4 NVIDIA V100 GPUs. |
| Software Dependencies | Yes | We conduct experiments on a machine with 4 NVIDIA Titan X GPUs, CUDA 9.2 and cu DNN 7.1.4 for most experiments. Baselines methods in this paper are reproduced by ourselves based on the Detectron framework (Girshick et al. 2018). |
| Experiment Setup | Yes | Specifically, the short side of input image is resized to 800, and the longer side is limited to 1,333. The data augmentation is simply flipping the images. For most of the original baselines, batch size on a single GPU is two images. Due to the limitation of GPU memory for CBNet, we put one image on each GPU for training the detectors using CBNet. Meanwhile, we set the initial learning rate as half of the default value and train for the same epochs as the original baselines. |