YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors-Reference-Cited by-同舟云学术

YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
Short-container-title:

Author:

Wang Chien-Yao¹,Bochkovskiy Alexey,Liao Hong-Yuan Mark¹

Affiliation:

1. Institute of Information Science, Academia Sinica,Taiwan

Funder

NCHC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10204762.pdf?arnumber=10204762

Reference97 articles.

1. Designing Network Design Spaces

2. DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

3. YOLO9000: Better, Faster, Stronger

4. You Only Look Once: Unified, Real-Time Object Detection

5. A ranking-based, balanced loss function unifying classification and localisation in object detection;oksuz;Advances in Neural IInformation Processing Systems,2020

Cited by 4380 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MSNet: Multi-Scale Network for Object Detection in Remote Sensing Images;Pattern Recognition;2025-02

2. Global attention module and cascade fusion network for steel surface defect detection;Pattern Recognition;2025-02

3. IFS-DETR: A real-time industrial fire smoke detection algorithm based on an end-to-end structured network;Measurement;2025-02

4. An algorithm for large-span flexible bridge pose estimation and multi-keypoint vibration displacement measurement;Measurement;2025-01

5. A novel image dehazing algorithm for complex natural environments;Pattern Recognition;2025-01