RepPoints v2: Verification Meets Regression for Object Detection
Authors: Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the challenging MS COCO 2017 benchmark [17], which is split into train, val and test-dev sets with 115K, 5K and 20K images, respectively. We train all the models using the train set and conduct an ablation study on the val set. A system-level comparison to other methods is reported on the test-dev set. |
| Researcher Affiliation | Collaboration | 1Center of Data Science, Peking University 2Microsoft Research Asia 3Key Laboratory of Machine Perception, MOE, School of EECS, Peking University 4Zhejiang Lab |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks that are clearly labeled as such. |
| Open Source Code | Yes | The code is available at https://github.com/Scalsol/Rep Points V2. |
| Open Datasets | Yes | We conduct experiments on the challenging MS COCO 2017 benchmark [17] |
| Dataset Splits | Yes | MS COCO 2017 benchmark [17], which is split into train, val and test-dev sets with 115K, 5K and 20K images, respectively. |
| Hardware Specification | Yes | the speed of Rep Points v1 is 12.7 FPS (img/s) using Res Net50 on a Titan XP GPU |
| Software Dependencies | No | We use the mmdetection codebase [2] for experiments. While mmdetection is mentioned, a specific version number for this software dependency is not provided. |
| Experiment Setup | Yes | All experiments perform training with an SGD optimizer on 8 GPUs with 2 images per GPU, using an initial learning rate of 0.01, a weight decay of 0.0001 and momentum of 0.9. In ablations, most experiments follow the 1x settings where 12 epochs with single-scale training of [800, 1333] are used, with learning rate decayed by 10 after epoch 8 and 11. |