Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Dual Relation Knowledge Distillation for Object Detection
Authors: Zhen-Liang Ni, Fukui Yang, Shengzhao Wen, Gang Zhang
IJCAI 2023 | Venue PDF | 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 specified, the ablation experiment usually adopts 1 learning schedule and the comparison experiment with other methods adopts 2 learning schedule. |