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About this paper
Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao
YOLOv4 introduces a highly optimized real-time object detector that achieves 43.5% AP on MS COCO at ~65 FPS on a Tesla V100 GPU, demonstrating that state-of-the-art performance can be attained while enabling training and deployment on conventional, single GPUs. This work from Alexey Bochkovskiy and researchers at Academia Sinica systematically aggregates and validates numerous deep learning features to push the efficiency-accuracy Pareto front.
Key insights
- State-of-the-art object detection can be achieved and democratized for training and deployment on a single conventional GPU.
- Systematic aggregation and empirical validation of existing deep learning features (termed 'Bag of Freebies' and 'Bag of Specials') is effective for performance optimization.
- Classifier accuracy does not directly translate to detector performance, and a comprehensive set of training techniques can make mini-batch size less critical for detector accuracy.
Summary by alphaXiv.
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