YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
Open accessAbstract
Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the-art methods. To address the topics, we propose a trainable bag-of-freebies oriented solution. We combine the flexible and efficient training tools with the proposed architecture and the compound scaling method. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 120 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. Source code is released in https://github.com/WongKinYiu/yolov7.
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After pre-training with Objects365, RT-DETR-R50 achieves 55.3% AP and RT-DETR-R101 achieves 56.2% AP.
“After pre-training with Objects365, RTDETR-R50 / R101 achieves 55 . 3% / 56 . 2% AP.”
From DETRs Beat YOLOs on Real-time Object Detection (2024) · cites this paper as [35]
RT-DETR-R101 is improved by 1.9% AP on COCO val2017 with Objects365 pre-training.
“Experimental results show that RT-DETR-R18/50/101 is improved by 2 . 7% / 2 . 2% / 1 . 9% AP on COCO val2017 .”
From DETRs Beat YOLOs on Real-time Object Detection (2024) · cites this paper as [35]
RT-DETR-R50 is improved by 2.2% AP on COCO val2017 with Objects365 pre-training.
“Experimental results show that RT-DETR-R18/50/101 is improved by 2 . 7% / 2 . 2% / 1 . 9% AP on COCO val2017 .”
From DETRs Beat YOLOs on Real-time Object Detection (2024) · cites this paper as [35]
RT-DETR-R18 is improved by 2.7% AP on COCO val2017 with Objects365 pre-training.
“Experimental results show that RT-DETR-R18/50/101 is improved by 2 . 7% / 2 . 2% / 1 . 9% AP on COCO val2017 .”
From DETRs Beat YOLOs on Real-time Object Detection (2024) · cites this paper as [35]
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Designing Network Design Spaces
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“However, in recent years, if we take object detection as an example, researchers often use the quality and distribution of prediction output by the network, and then consider together with the ground truth to use some calculation and optimization methods to generate a reliable soft label [61, 8, 36, 99, 91, 44, 43, 90…”
MethodDINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
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“Being able to become a state-of-the-art real-time object detector usually requires the following characteristics: (1) a faster and stronger network architecture; (2) a more effective feature integration method [22, 97, 37, 74, 59, 30, 9, 45]; (3) a more accurate detection method [76, 77, 69]; (4) a more robust loss fu…”
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MethodExploring Plain Vision Transformer Backbones for Object Detection
Li, Y., Mao, H., Girshick, R., & He, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_17 · Cited by 902 · see citing works
Averaging Weights Leads to Wider Optima and Better GeneralizationPreprint
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Snapshot Ensembles: Train 1, get M for freePreprint
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MethodDiverse Branch Block: Building a Convolution as an Inception-like Unit
Ding, X., Zhang, X., Han, J., & Ding, G. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01074 · Cited by 566 · cited 2× · see citing works
“Model re-parametrization techniques [71, 31, 75, 19, 33, 11, 4, 24, 13, 12, 10, 29, 14, 78] merge multiple computational modules into one at inference stage.”
BackgroundMethodOTA: Optimal Transport Assignment for Object Detection
Ge, Z., Liu, S., Li, Z., Yoshie, O., & Sun, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00037 · Cited by 560 · cited 3× · see citing works
“However, in recent years, if we take object detection as an example, researchers often use the quality and distribution of prediction output by the network, and then consider together with the ground truth to use some calculation and optimization methods to generate a reliable soft label [61, 8, 36, 99, 91, 44, 43, 90…”
BackgroundMethodAn Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
Lee, Y., Hwang, J., Lee, S., Bae, Y., & Park, J. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00103 · Cited by 542 · cited 4× · see citing works
“The design of CSPVoVNet [79] in Figure 2 (b) is a variation of VoVNet [39].”
MethodInfluentialProbabilistic Anchor Assignment with IoU Prediction for Object Detection
Kim, K., & Lee, H. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_22 · Cited by 529 · cited 1× · see citing works
“However, in recent years, if we take object detection as an example, researchers often use the quality and distribution of prediction output by the network, and then consider together with the ground truth to use some calculation and optimization methods to generate a reliable soft label [61, 8, 36, 99, 91, 44, 43, 90…”
MethodChoi, J., Chun, D., Kim, H., & Lee, H.-J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00059 · Cited by 505 · cited 1× · see citing works
“However, in recent years, if we take object detection as an example, researchers often use the quality and distribution of prediction output by the network, and then consider together with the ground truth to use some calculation and optimization methods to generate a reliable soft label [61, 8, 36, 99, 91, 44, 43, 90…”
MethodIoU Loss for 2D/3D Object Detection
Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., & Yang, R. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00019 · Cited by 483 · cited 1× · see citing works
“Being able to become a state-of-the-art real-time object detector usually requires the following characteristics: (1) a faster and stronger network architecture; (2) a more effective feature integration method [22, 97, 37, 74, 59, 30, 9, 45]; (3) a more accurate detection method [76, 77, 69]; (4) a more robust loss fu…”
MethodXiang, L., Wang, W., Hu, X., Li, J., Tang, J., & Yang, J. J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01146 · Cited by 476 · cited 1× · see citing works
“However, in recent years, if we take object detection as an example, researchers often use the quality and distribution of prediction output by the network, and then consider together with the ground truth to use some calculation and optimization methods to generate a reliable soft label [61, 8, 36, 99, 91, 44, 43, 90…”
MethodMCUNet: Tiny Deep Learning on IoT DevicesPreprint
Lin, J., Chen, W.-M., Lin, Y., Cohn, J., Gan, C., & Han, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10319 · Cited by 425 · cited 2× · see citing works
“For example, the devel-Figure opment of MCUNet [49, 48] and NanoDet [54] focused on producing low-power single-chip and improving the inference speed on edge CPU.”
BackgroundYou Only Learn One Representation: Unified Network for Multiple TasksPreprint
Wang, C.-Y., Yeh, I.-H., & Liao, H.-Y. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04206 · Cited by 383 · cited 5× · see citing works
“As for methods such as YOLOX [20] and YOLOR [79], they focus on improving the inference speed of various GPUs.”
BackgroundMethodInfluentialSelf-citationGS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
Li, B., Ouyang, W., Sheng, L., Zeng, X., & Wang, X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00111 · Cited by 360 · cited 1× · see citing works
“For example, multi-object tracking [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc.”
BackgroundEnsemble deep learning in bioinformatics
Cao, Y., Geddes, T. A., Yang, J. Y. H., & Yang, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0217-y · Cited by 357 · cited 1× · see citing works
“Model re-parametrization techniques [71, 31, 75, 19, 33, 11, 4, 24, 13, 12, 10, 29, 14, 78] merge multiple computational modules into one at inference stage.”
MethodSparse DETR: Efficient End-to-End Object Detection with Learnable SparsityPreprint
Roh, B., Shin, J., Shin, W., & Kim, S. (2021) · International Conference on Learning Representations · doi:10.48550/arxiv.2111.14330 · Cited by 329 · cited 1× · see citing works
“Even for architectures such as ResNet [26] and DenseNet [32] which usually converge well, deep supervision [70, 98, 67, 47, 82, 65, 86, 50] can still significantly improve the performance of the model on many tasks.”
PP-YOLOE: An evolved version of YOLOPreprint
Xu, S., Wang, X., Lv, W., Chang, Q., Cui, C., Deng, K., Wang, G., Dang, Q., Wei, S., Du, Y., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.16250 · Cited by 301 · cited 3× · see citing works
“We find that the design of RepCSPResNet [85] also fit our design pattern.”
MethodRevisiting ResNets: Improved Training and Scaling StrategiesPreprint
Bello, I., Fedus, W., Du, X., Cubuk, E. D., Srinivas, A., Lin, T.-Y., Shlens, J., & Zoph, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07579 · Cited by 257 · see citing works
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., & Wilson, A. G. (2018) · arXiv · doi:10.48550/arxiv.1802.10026 · Cited by 252 · cited 1× · see citing works
“Model re-parametrization techniques [71, 31, 75, 19, 33, 11, 4, 24, 13, 12, 10, 29, 14, 78] merge multiple computational modules into one at inference stage.”
MethodEnd-to-End Object Detection with Fully Convolutional Network
Wang, J., Song, L., Li, Z., Sun, H., Sun, J., & Zheng, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01559 · Cited by 235 · cited 2× · see citing works
“Even for architectures such as ResNet [26] and DenseNet [32] which usually converge well, deep supervision [70, 98, 67, 47, 82, 65, 86, 50] can still significantly improve the performance of the model on many tasks.”
MethodAutoAssign: Differentiable Label Assignment for Dense Object DetectionPreprint
Zhu, B., Wang, J., Jiang, Z., Zong, F., Liu, S., Li, Z., & Sun, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03496 · Cited by 208 · cited 1× · see citing works
“Being able to become a stateof-the-art real-time object detector usually requires the following characteristics: (1) a faster and stronger network architecture; (2) a more effective feature integration method [8, 21, 29, 36, 43, 56, 71, 94]; (3) a more accurate detection method [66, 73, 74]; (4) a more robust loss fun…”
MethodCLU-CNNs: Object detection for medical images
Li, Z., Dong, M., Wen, S., Hu, X. Y., Zhou, P., & Zeng, Z. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.04.028 · Cited by 205 · cited 1× · see citing works
“For example, multi-object tracking [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc.”
BackgroundCBNet: A Composite Backbone Network Architecture for Object Detection
Liang, T., Chu, X., Liu, Y., Wang, Y., Tang, Z., Chu, W., Chen, J., & Ling, H. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3216771 · Cited by 194 · see citing works
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., Kindermans, P.-J., Tan, M., Singh, V., & Chen, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00382 · Cited by 193 · cited 4× · see citing works
“The computing devices that execute real-time object detection is usually some mobile CPU or GPU, as well as various neural processing units (NPU) developed by major manufacturers.”
MethodInfluentialDesigning Network Design Strategies Through Gradient Path AnalysisPreprint
Wang, C.-Y., Liao, H.-Y. M., & Yeh, I.-H. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.04800 · Cited by 183 · see citing works
Self-citationA2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation
Hu, M., Li, Y., Fang, L., & Wang, S. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01509 · Cited by 146 · cited 1× · see citing works
“Being able to become a state-of-the-art real-time object detector usually requires the following characteristics: (1) a faster and stronger network architecture; (2) a more effective feature integration method [22, 97, 37, 74, 59, 30, 9, 45]; (3) a more accurate detection method [76, 77, 69]; (4) a more robust loss fu…”
MethodA Dual Weighting Label Assignment Scheme for Object Detection
Li, S., He, C., Li, R., & Lei Zhang, A. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00917 · Cited by 138 · cited 3× · see citing works
“However, in recent years, if we take object detection as an example, researchers often use the quality and distribution of prediction output by the network, and then consider together with the ground truth to use some calculation and optimization methods to generate a reliable soft label [61, 8, 36, 99, 91, 44, 43, 90…”
BackgroundMethodObject Detection Approach for Robot Grasp Detection
Karaoğuz, H., & Jensfelt, P. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793751 · Cited by 125 · cited 1× · see citing works
“For example, multi-object tracking [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc.”
Background3D-MAN: 3D Multi-frame Attention Network for Object Detection
Yang, Z., Zhou, Y., Chen, Z., & Ngiam, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00190 · Cited by 117 · cited 1× · see citing works
“Even for architectures such as ResNet [26] and DenseNet [32] which usually converge well, deep supervision [70, 98, 67, 47, 82, 65, 86, 50] can still significantly improve the performance of the model on many tasks.”
Fast and Accurate Model Scaling
Dollár, P., Singh, M., & Girshick, R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00098 · Cited by 116 · cited 3× · see citing works
“In [15], the researcher analyzes the relationship between scaling factors and the amount of parameters and operations, trying to directly estimate some rules, and thereby obtain the scaling factors required by model scaling.”
BackgroundMethodPP-PicoDet: A Better Real-Time Object Detector on Mobile Devices
Yu, G., Chang, Q., Lv, W., Xu, C., Cui, C., Ji, W., Dang, Q., Deng, K., Wang, G., Du, Y., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.00902 · Cited by 112 · see citing works
Object Detection from Scratch with Deep Supervision
Shen, Z., Liu, Z., Li, J., Jiang, Y., Chen, Y., & Xue, X. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2922181 · Cited by 105 · cited 1× · see citing works
“Even for architectures such as ResNet [26] and DenseNet [32] which usually converge well, deep supervision [70, 98, 67, 47, 82, 65, 86, 50] can still significantly improve the performance of the model on many tasks.”
Online Convolutional Reparameterization
Hu, M. H., Feng, J., Hua, J., Lai, B., Huang, J., Gong, X., & Hua, X. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00065 · Cited by 104 · see citing works
Jaeger, P. F., Köhl, S., Bickelhaupt, S., Isensee, F., Kuder, T. A., Schlemmer, H., & Maier-Hein, K. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08661 · Cited by 97 · see citing works
AP-Loss for Accurate One-Stage Object Detection
Chen, K., Lin, W., Li, J., See, J., Wang, J., & Zou, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2991457 · Cited by 86 · cited 1× · see citing works
“Being able to become a state-of-the-art real-time object detector usually requires the following characteristics: (1) a faster and stronger network architecture; (2) a more effective feature integration method [22, 97, 37, 74, 59, 30, 9, 45]; (3) a more accurate detection method [76, 77, 69]; (4) a more robust loss fu…”
MethodYOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
Liu, Y., Wang, L., & Liu, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561423 · Cited by 85 · cited 1× · see citing works
“Even for architectures such as ResNet [26] and DenseNet [32] which usually converge well, deep supervision [70, 98, 67, 47, 82, 65, 86, 50] can still significantly improve the performance of the model on many tasks.”
Rank & Sort Loss for Object Detection and Instance Segmentation
Öksüz, K., Çam, B. C., Akbaş, E., & Kalkan, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00300 · Cited by 54 · cited 1× · see citing works
“Being able to become a state-of-the-art real-time object detector usually requires the following characteristics: (1) a faster and stronger network architecture; (2) a more effective feature integration method [22, 97, 37, 74, 59, 30, 9, 45]; (3) a more accurate detection method [76, 77, 69]; (4) a more robust loss fu…”
MethodExpandNets: Linear Over-parameterization to Train Compact Convolutional NetworksPreprint
Guo, S., Álvarez, J. M., & Salzmann, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10495 · Cited by 49 · see citing works
Memory-efficient Patch-based Inference for Tiny Deep Learning
Lin, J., Chen, W.-M., Cai, H., Gan, C., & Han, S. (2021) · neural information processing systems · Cited by 38 · see citing works
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object DetectionPreprint
Öksüz, K., Çam, B. C., Akbaş, E., & Kalkan, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13592 · Cited by 33 · cited 1× · see citing works
“Being able to become a stateof-the-art real-time object detector usually requires the following characteristics: (1) a faster and stronger network architecture; (2) a more effective feature integration method [8, 21, 29, 36, 43, 56, 71, 94]; (3) a more accurate detection method [66, 73, 74]; (4) a more robust loss fun…”
MethodRe-parameterizing Your Optimizers rather than Architectures
Ding, X., Chen, H., Zhang, X., Huang, K., Han, J., & Ding, G. (2022) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.48550/arxiv.2205.15242 · Cited by 33 · see citing works
Paul, S. K., Chowdhury, M. T., Nicolescu, M., Nicolescu, M. N., & Feil-Seifer, D. (2021) · Transactions on computational science and computational intelligence · doi:10.1007/978-3-030-71051-4_10 · Cited by 32 · cited 1× · see citing works
“For example, multi-object tracking [94, 93], autonomous driving [40, 18], robotics [35, 58], medical image analysis [34, 46], etc.”
BackgroundSimple Training Strategies and Model Scaling for Object DetectionPreprint
Du, X., Zoph, B., Hung, W.-C., & Lin, T.-Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00057 · Cited by 28 · cited 1× · see citing works
“Model scaling [72, 60, 74, 73, 15, 16, 2, 51] is a way to scale up or down an already designed model and make it fit in different computing devices.”
BackgroundFBNetV5: Neural Architecture Search for Multiple Tasks in One RunPreprint
Wu, B., Li, C., Zhang, H., Dai, X., Zhang, P., Yu, M., Wang, J., Lin, Y., & Vajda, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10007 · Cited by 23 · cited 4× · see citing works
“The computing devices that execute real-time object detection is usually some mobile CPU or GPU, as well as various neural processing units (NPU) developed by major manufacturers.”
MethodInfluential
Flagged first, then by citations
7 of the 103 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- lyu, 2021, NanoDet-Plus
- glenn, 2022, Yolov5 Release v6 1
- hu, 0, Online convolutional re-parameterization, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- zhou, 2019, Objects as points, ArXiv Preprint
- kumar, 2022, An improved one millisecond mobile backbone, ArXiv Preprint
Cited by
Sorted by citations
DETRs Beat YOLOs on Real-time Object Detection
Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., & Chen, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01605 · Cited by 5,020 · see citing works
“We utilize the benchmark to test the end-to-end speed of anchor-based detectors YOLOv5 [10] and YOLOv7 [35], as well as anchor-free detectors PP-YOLOE [37], YOLOv6 [15] and YOLOv8 [11] on T4 GPU with TensorRT FP16.”
BackgroundMethodInfluentialYOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
Wang, C.-Y., Yeh, I.-H., & Mark Liao, H.-Y. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72751-1_1 · Cited by 4,823 · see citing works
Self-citationObject Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689 · see citing works
“Recently, YOLOv7 [52], a follow-up work from YOLOv4 team, has been proposed.”
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
Terven, J. R., Esparza, D. M. C., & Romero-González, J.-A. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5040083 · Cited by 3,681 · see citing works
ByteTrack: Multi-object Tracking by Associating Every Detection Box
Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., & Wang, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_1 · Cited by 2,793 · see citing works
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et al. (2022) · arXiv · doi:10.48550/arxiv.2209.02976 · Cited by 1,792 · see citing works
“We notice that a half-stride gray border is put around each image when evaluating the model performance in the implementations of YOLOv5 [10] and YOLOv7 [42].”
BackgroundMethodInfluentialSlim-neck by GSConv: a lightweight-design for real-time detector architectures
Li, H.-L., Li, J., Wei, H., Liu, Z., Zhan, Z., & Ren, Q. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01436-6 · Cited by 1,032 · see citing works
A Review on YOLOv8 and Its Advancements
Sohan, M., Sai Ram, T., & Rami Reddy, C. V. (2024) · Algorithms for Intelligent Systems · doi:10.1007/978-981-99-7962-2_39 · Cited by 898 · see citing works
YOLO-World: Real-Time Open-Vocabulary Object Detection
Cheng, T., Song, L., Ge, Y., Liu, W., Wang, X., & Shan, Y. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01599 · Cited by 815 · see citing works
DC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor
Lou, H., Duan, X., Guo, J., Liu, H., Gu, J. J., Bi, L., & Chen, H. (2023) · Electronics · doi:10.3390/electronics12102323 · Cited by 660 · see citing works
AFPN: Asymptotic Feature Pyramid Network for Object Detection
Yang, G., Lei, J., Zhu, Z., Cheng, S., Feng, Z., & Liang, R. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394415 · Cited by 592 · see citing works
ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation
Kang, M., Ting, C., Ting, F. F., & Phan, R. C. ‐. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105057 · Cited by 520 · see citing works
YOLO-based Object Detection Models: A Review and its Applications
Vijayakumar, A., & Subramaniyaswamy, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18872-y · Cited by 516 · see citing works
YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO
Hussain, M. (2024) · IEEE Access · doi:10.1109/access.2024.3378568 · Cited by 492 · see citing works
Mixed local channel attention for object detection
Wan, D., Lu, R., Shen, S., Xu, T., Lang, X., & Ren, Z. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106442 · Cited by 439 · see citing works
Deep learning-based structural health monitoring
Cha, Y., Ali, R., Lewis, J., & Büyüköztürk, O. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105328 · Cited by 464 · see citing works
FFCA-YOLO for Small Object Detection in Remote Sensing Images
Zhang, Y., Ye, M., Zhu, G., Liu, Y., Guo, P., & Yan, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3363057 · Cited by 439 · see citing works
Badgujar, C. M., Poulose, A., & Gan, H. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109090 · Cited by 411 · see citing works
Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation
Feng, Y., Huang, J., Du, S., Ying, S., Yong, J., Li, Y., Ding, G., Ji, R., & Gao, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3524377 · Cited by 431 · see citing works
Sapkota, R., Flores-Calero, M., Qureshi, R., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 454 · see citing works
Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review
Amjoud, A. B., & Amrouch, M. (2023) · IEEE Access · doi:10.1109/access.2023.3266093 · Cited by 359 · see citing works
Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing
Hao, Y., Liu, B., Zhao, B. C., & Liu, E. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3339235 · Cited by 327 · see citing works
Drone-YOLO: An Efficient Neural Network Method for Target Detection in Drone Images
Zhang, Z. (2023) · Drones · doi:10.3390/drones7080526 · Cited by 326 · see citing works
BL-YOLOv8: An Improved Road Defect Detection Model Based on YOLOv8
Wang, X., Gao, H., Jia, Z., & Li, Z. (2023) · Sensors · doi:10.3390/s23208361 · Cited by 328 · see citing works
Remote Sensing Object Detection in the Deep Learning Era—A Review
Gui, S., Song, S., Qin, R., & Tang, Y. (2024) · Remote Sensing · doi:10.3390/rs16020327 · Cited by 328 · see citing works
MURF: Mutually Reinforcing Multi-Modal Image Registration and Fusion
Xu, H., Yuan, J., & Ma, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3283682 · Cited by 303 · see citing works
DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects
Zhang, Y., Zhang, H., Huang, Q., Han, Y., & Zhao, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122669 · Cited by 293 · see citing works
Yao, S., Guan, R., Huang, X., Li, Z., Sha, X., Yue, Y., Lim, E. G., Seo, H., Man, K. L., Zhu, X., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3307157 · Cited by 287 · see citing works
LDConv: Linear deformable convolution for improving convolutional neural networks
Zhang, X., Song, Y., Song, T., Yang, D., Ye, Y., Zhou, J. N., & Zhang, L. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105190 · Cited by 316 · see citing works
Roy, A. M., & Bhaduri, J. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102007 · Cited by 309 · see citing works
Du, B., Huang, Y., Chen, J., & Huang, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01291 · Cited by 276 · see citing works
Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm
Ju, R., & Cai, W. (2023) · Scientific Reports · doi:10.1038/s41598-023-47460-7 · Cited by 278 · see citing works
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection
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A comprehensive survey of deep learning-based lightweight object detection models for edge devices
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Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images
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An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation
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YOLO-HMC: An Improved Method for PCB Surface Defect Detection
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Insulator-Defect Detection Algorithm Based on Improved YOLOv7
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MFFSODNet: Multiscale Feature Fusion Small Object Detection Network for UAV Aerial Images
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Detection of Camellia oleifera Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation
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A Comprehensive Review of Deep Learning-Based PCB Defect Detection
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Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
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The evolution of object detection methods
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YOLO-MIF: Improved YOLOv8 with Multi-Information fusion for object detection in Gray-Scale images
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CFANet: Efficient Detection of UAV Image Based on Cross-Layer Feature Aggregation
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An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings
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UAV T-YOLO-Rice: An Enhanced Tiny Yolo Networks for Rice Leaves Diseases Detection in Paddy Agronomy
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Faster and Lightweight: An Improved YOLOv5 Object Detector for Remote Sensing Images
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An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images
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E-YOLO: Recognition of estrus cow based on improved YOLOv8n model
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Global to Local: A Scale-Aware Network for Remote Sensing Object Detection
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SOD-YOLOv8—Enhancing YOLOv8 for Small Object Detection in Aerial Imagery and Traffic Scenes
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RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
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- Authors
- Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M.
- Affiliations
- Institute of Information Science, Academia Sinica (TW)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 7464-7475
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076325
- Cited by
- 13,198
- References
- 103
- Citations by year
- 2026: 1908 · 2025: 4113 · 2024: 4600 · 2023: 2384 · 2022: 192
- FWCI
- 1392.15
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NCHC
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- YOLOv7, real-time object detection, compound scaling, architecture optimization, detection speed and accuracy
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