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 | Conference PDF | Archive PDF | Plain Text | 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. |