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..
YOLOv10: Real-Time End-to-End Object Detection
Authors: Ao Wang, Hui Chen, Lihao Liu, Kai CHEN, Zijia Lin, Jungong Han, guiguang ding
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8 faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8 smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46% less latency and 25% fewer parameters for the same performance. |
| Researcher Affiliation | Academia | Ao Wang1 Hui Chen2 Lihao Liu1 Kai Chen1 Zijia Lin1 Jungong Han3 Guiguang Ding1 1School of Software, Tsinghua University 2BNRist, Tsinghua University 3Department of Automation, Tsinghua University |
| Pseudocode | Yes | Algorithm 1: Rank-guided block design |
| Open Source Code | Yes | Code and models are available at https://github.com/THU-MIG/yolov10. |
| Open Datasets | Yes | Extensive experiments on standard benchmarks for object detection, i.e., COCO [35], demonstrate that our YOLOv10 can significantly outperform previous state-of-the-art models in terms of computation-accuracy trade-offs across various model scales. [...] the data is available at https://www.cocodataset.org/. |
| Dataset Splits | Yes | We verify the proposed detector on COCO [35] under the same training-from-scratch setting [21, 65, 62]. Moreover, the latencies of all models are tested on T4 GPU with Tensor RT FP16, following [78]. ... We report the standard mean average precision (AP) across different object scales and Io U thresholds on the COCO dataset [35]. |
| Hardware Specification | Yes | All models are trained on 8 NVIDIA 3090 GPUs. ...the latencies of all models are tested on T4 GPU with Tensor RT FP16... ...on CPU (Intel Xeon Skylake, IBRS) using Open VINO |
| Software Dependencies | No | The paper mentions 'Tensor RT FP16' and 'Open VINO' but does not specify version numbers for these or other relevant software libraries (e.g., PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | We employ the consistent dual assignments for NMS-free training and perform holistic efficiency-accuracy driven model design based on it, which brings our YOLOv10 models. ... We verify the proposed detector on COCO [35] under the same training-from-scratch setting [21, 65, 62]. ... Tab. 14 presents the detailed hyper-parameters. hyper-parameter YOLOv10-N/S/M/B/L/X epochs 500 optimizer SGD momentum 0.937 weight decay 5 10 4 warm-up epochs 3 ... initial learning rate 10 2 final learning rate 10 4 |