all AI news
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. (arXiv:2207.02696v1 [cs.CV])
July 7, 2022, 1:12 a.m. | Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao
cs.CV updates on arXiv.org arxiv.org
YOLOv7 surpasses all known object detectors in both speed and accuracy in the
range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all
known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6
object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based
detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed
and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask
R-CNN (8.6 FPS A100, 55.2% AP) …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Program Control Data Analyst
@ Ford Motor Company | Mexico
Vice President, Business Intelligence / Data & Analytics
@ AlphaSense | Remote - United States