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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Efficient Object Detection Models with Knowledge Distillation
Authors: Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive empirical evaluation with different distillation configurations over multiple datasets including PASCAL, KITTI, ILSVRC and MS-COCO. |
| Researcher Affiliation | Collaboration | 1NEC Labs America 2University of Missouri 3University of California, San Diego |
| Pseudocode | No | The paper does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Datasets We evaluate our method on several commonly used public detection datasets, namely, KITTI [12], PASCAL VOC 2007 [11], MS COCO [6] and Image Net DET benchmark (ILSVRC 2014) [35]. |
| Dataset Splits | Yes | Since KITTI and ILSVRC 2014 do not provide ground-truth annotation for test sets, we use the training/validation split introduced by [39] and [24] for analysis. |
| Hardware Specification | No | The paper mentions running experiments "on GPU" but does not specify any particular GPU models, CPU models, memory details, or other specific hardware specifications. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with their specific version numbers. |
| Experiment Setup | Yes | We fix them [λ and γ] to be 1 and 0.5, respectively, throughout the experiments. For example, we use w0 = 1.5 for the background class and wi = 1 for all the others in experiments on the PASCAL dataset. ... ν is a weight parameter (set as 0.5 in our experiments). |