Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Instance-Conditional Knowledge Distillation for Object Detection
Authors: Zijian Kang, Peizhen Zhang, Xiangyu Zhang, Jian Sun, Nanning Zheng
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,926 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 7,170 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the efficacy of our method: we observe impressive improvements under various settings. We perform comprehensive experiments on challenging benchmarks. Results demonstrate impressive improvements over various detectors with up to 4 AP gain in MS-COCO, including recent detectors for instance segmentation [41, 46, 16]. |
| Researcher Affiliation | Collaboration | Zijian Kang Xi an Jiaotong University EMAIL; Peizhen Zhang MEGVII Technology EMAIL; Xiangyu Zhang MEGVII Technology EMAIL; Jian Sun MEGVII Technology EMAIL; Nanning Zheng Xi an Jiaotong University EMAIL |
| Pseudocode | No | The paper includes mathematical formulations and architectural diagrams (e.g., Figure 1, Figure 2), but does not contain structured pseudocode blocks or sections explicitly labeled 'Algorithm'. |
| Open Source Code | Yes | Code has been released on https://github.com/megvii-research/ICD. |
| Open Datasets | Yes | Most experiments are conducted on a large scale object detection benchmark MS-COCO 4[31] with 80 classes. MS-COCO is publicly available, the annotations are licensed under a Creative Commons Attribution 4.0 License and the use of the images follows Flickr Terms of Use. Refer to [31] for more details. |
| Dataset Splits | Yes | We train models on MS-COCO 2017 trainval115k subset and validate on minival subset. |
| Hardware Specification | Yes | All experiments are running on eight 2080ti GPUs with 2 images in each. Specifically, we benchmark on 1 schedule on Retina Net [30] with eight 2080ti, following the same configuration in Section 4.2. |
| Software Dependencies | No | We conduct experiments on Pytorch [34] with the widely used Detectron2 library [47] and Adelai Det library 3 [40]. While these software components are mentioned, specific version numbers for PyTorch, Detectron2, or AdelaiDet are not provided in the text. |
| Experiment Setup | Yes | We adopt the 1 schedule, which denotes 9k iterations of training, following the standard protocols in Detectron2 unless otherwise specified. For distillation, the hyper-parameter λ is set to 8 for one-stage detectors and 3 for two-stage detectors respectively. To optimize the transformer decoder, we adopt Adam W optimizer [33] for the decoder and MLPs following common settings for transformer [43, 5]. Corresponding hyper-parameters follows [5], where the initial learning rate and weight decay are set to 1e-4. We adopt the 256 hidden dimension for our decoder and all MLPs, the decoder has 8 heads in parallel. |