Search Results for author: Cheng Cui

Found 12 papers, 10 papers with code

DETRs Beat YOLOs on Real-time Object Detection

4 code implementations17 Apr 2023 Wenyu Lv, Yian Zhao, Shangliang Xu, Jinman Wei, Guanzhong Wang, Cheng Cui, Yuning Du, Qingqing Dang, Yi Liu

In this paper, we first analyze the influence of NMS in modern real-time object detectors on inference speed, and establish an end-to-end speed benchmark.

Object object-detection +1

2nd Place Solution to Google Landmark Retrieval 2020

no code implementations11 Jul 2022 Min Yang, Cheng Cui, Xuetong Xue, Hui Ren, Kai Wei

Using this method, we got a public score of 0. 40176 and a private score of 0. 36278 and achieved 2nd place in the Google Landmark Retrieval Competition 2020.

Retrieval

PP-YOLOE: An evolved version of YOLO

8 code implementations30 Mar 2022 Shangliang Xu, Xinxin Wang, Wenyu Lv, Qinyao Chang, Cheng Cui, Kaipeng Deng, Guanzhong Wang, Qingqing Dang, Shengyu Wei, Yuning Du, Baohua Lai

In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.

Dense Object Detection Multi-Object Tracking +3

PP-LCNet: A Lightweight CPU Convolutional Neural Network

8 code implementations17 Sep 2021 Cheng Cui, Tingquan Gao, Shengyu Wei, Yuning Du, Ruoyu Guo, Shuilong Dong, Bin Lu, Ying Zhou, Xueying Lv, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma

We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks.

Image Classification object-detection +2

HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

2 code implementations15 Oct 2020 Pengcheng Yuan, Shufei Lin, Cheng Cui, Yuning Du, Ruoyu Guo, Dongliang He, Errui Ding, Shumin Han

Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications.

Image Classification Image Segmentation +5

2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019

2 code implementations10 Jun 2019 Kaibing Chen, Cheng Cui, Yuning Du, Xianglong Meng, Hui Ren

We present a retrieval based system for landmark retrieval and recognition challenge. There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching.

Landmark Recognition Retrieval

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