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..
An Empirical Study of Adder Neural Networks for Object Detection
Authors: Xinghao Chen, Chang Xu, Minjing Dong, Chunjing XU, Yunhe Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we present an empirical study of Adder Nets for object detection. We present extensive ablation studies to explore several design choices of adder detectors. Comparisons with state-of-the-arts are conducted on COCO and PASCAL VOC benchmarks. |
| Researcher Affiliation | Collaboration | 1Huawei Noah s Ark Lab 2School of Computer Science, University of Sydney |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public release of source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on the bounding box detection track of MS COCO 2017 and PASCAL VOC benchmarks, which have 80 and 20 object classes, respectively. |
| Dataset Splits | Yes | for COCO benchmark we use the COCO train2017 split that contains 118k images for training, val2017 split for validation (5k) and test-dev split (20k) for testing. |
| Hardware Specification | No | The paper mentions using GPUs (e.g., 'on one GPU', 'over 8 GPUs') but does not provide specific models or other hardware details for the experimental setup. |
| Software Dependencies | No | The paper mentions 'MMDetection [6]' as the base framework for implementation, but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | All models are trained with stochastic gradient descent (SGD) over 8 GPUs. Unless otherwise specified, all models are trained for 12 epochs (also known as 1 schedule) with cosine learning rate decay strategy. Weight decay and momentum are set to 0.0001 and 0.9, respectively. |