DeRPN: Taking a Further Step toward More General Object Detection
Authors: Lele Xie, Yuliang Liu, Lianwen Jin, Zecheng Xie9046-9053
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments conducted on both general object detection datasets (Pascal VOC 2007, 2012 and MS COCO) and scene text detection datasets (ICDAR 2013 and COCO-Text) all prove that our De RPN can significantly outperform RPN. |
| Researcher Affiliation | Academia | Lele Xie, Yuliang Liu, Lianwen Jin, Zecheng Xie School of Electronic and Information Engineering, South China University of Technology xie.lele@mail.scut.edu.cn, liu.yuliang@mail.scut.edu.cn, eelwjin@scut.edu.cn, zchengxie@gmail.com |
| Pseudocode | No | The paper describes the methods and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code has been released at https://github.com/HCIILAB/De RPN. |
| Open Datasets | Yes | Comprehensive experiments conducted on both general object detection datasets (Pascal VOC 2007, 2012 and MS COCO) and scene text detection datasets (ICDAR 2013 and COCO-Text) all prove that our De RPN can significantly outperform RPN. |
| Dataset Splits | Yes | Experiments on MS COCO In addition, we verified our method on MS COCO 2017, which consists of a training set ( 118k images), test set ( 20k images) and validation set (5k images). |
| Hardware Specification | Yes | We also evaluated the inference time on a single TITAN XP GPU. |
| Software Dependencies | No | The paper mentions general frameworks like CNN but does not specify software names with version numbers for reproducibility. |
| Experiment Setup | Yes | The settings of RPN, training, and testing followed that of (Ren et al. 2015)... We set the anchor strings as a geometric progression (denoted as {an}), i.e., (16, 32, 64, 128, 256, 512, 1024)... β is used to adjust magnitude of interval, which is intended to be 0.1 in our experiments... λ is a balancing parameter for Lcls and Lreg, which is empirically set to 10. For each scale, we randomly sample at most 30 positive and negative anchor strings to form a mini-batch. |