Dual Relation Knowledge Distillation for Object Detection
Authors: Zhen-Liang Ni, Fukui Yang, Shengzhao Wen, Gang Zhang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method achieves state-of-the-art performance, which improves Faster R-CNN based on Res Net50 from 38.4% to 41.6% m AP and improves Retina Net based on Res Net50 from 37.4% to 40.3% m AP on COCO 2017. ... 4 Experimental and Results |
| Researcher Affiliation | Collaboration | Zhen-Liang Ni1 , Fukui Yang2 , Shengzhao Wen2 , Gang Zhang2 1 Institute of Automation, Chinese Academy of Sciences 2 Department of Computer Vision Technology (VIS), Baidu Inc. |
| Pseudocode | Yes | Algorithm 1 Dual Relation Knowledge Distillation |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | COCO2017 [Lin et al., 2014] is used to evaluate our method, which is a challenging dataset in object detection. It contains 120k images and 80 object classes. |
| Dataset Splits | No | The paper uses COCO2017 for evaluation and mentions training strategies like "12 epochs" or "24 epochs", but it does not explicitly provide specific train/validation/test dataset splits or percentages. |
| Hardware Specification | Yes | All experiments are performed on 8 Tesla P40 GPUs. |
| Software Dependencies | No | The paper mentions using 'SGD' as an optimizer but does not provide specific version numbers for any software, libraries, or frameworks. |
| Experiment Setup | Yes | The batch size is set to 16. The initial learning rate is 0.02. The momentum is set to 0.9 and the weight decay is 0.0001. Unless speciļ¬ed, the ablation experiment usually adopts 1 learning schedule and the comparison experiment with other methods adopts 2 learning schedule. |