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..
A Transformer-Based Object Detector with Coarse-Fine Crossing Representations
Authors: Zhishan Li, Ying Nie, Kai Han, Jianyuan Guo, Lei Xie, Yunhe Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the COCO dataset demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1College of Control Science and Engineering, Zhejiang University 2Huawei Noah s Ark Lab |
| Pseudocode | No | The paper provides mathematical formulations and architectural diagrams but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be available at https: //gitee.com/mindspore/models/tree/master/research/cv/CFDT. |
| Open Datasets | Yes | We conduct experiments on Microsoft COCO 2017 benchmark [1]. |
| Dataset Splits | Yes | Following the usual practice, 118K images are used for training, 5K images for testing. |
| Hardware Specification | Yes | We gratefully acknowledge the support of Mind Spore [53], CANN(Compute Architecture for Neural Networks) and Ascend AI Processor used for this research. |
| Software Dependencies | No | The paper mentions software frameworks like Mind Spore and CANN but does not provide specific version numbers for them. |
| Experiment Setup | Yes | We follow the training strategy provided in Vi DT [14], including Adam W [45] with the initial learning rate of 1 10 4, training with multi-scale input sizes and the total training epoch is set as 50. ... the training batch size per card of P-Tiny and P-Small is set as 2, while that of P-Medium is 1. |