An Empirical Study of Adder Neural Networks for Object Detection
Authors: Xinghao Chen, Chang Xu, Minjing Dong, Chunjing XU, Yunhe Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 speciļ¬ed, 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. |