YOLOv10 vs YOLOv4

YOLOv10 is a real-time end-to-end object detection model developed by THU-MIG at Tsinghua University, released in May 2024 under the AGPL-3.0 license. It introduces consistent dual assignments during training — using both one-to-many and one-to-one label assignment strategies — to eliminate the need for non-maximum suppression at inference time while maintaining competitive accuracy. This end-to-end design reduces inference latency compared to NMS-dependent detectors at similar accuracy levels.

YOLOv10-B achieves 52.7% AP on COCO with 46% lower latency than YOLOv9-C at comparable performance. The model is available in six sizes from Nano to Extra Large, built on the Ultralytics framework, and exportable to ONNX, TensorRT, and CoreML. YOLOv10 is suited for latency-sensitive deployment scenarios where post-processing overhead is a constraint.

YOLOv4 is an object detection model developed by Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao at Academia Sinica, released in April 2020 via the Darknet framework. It combines a CSPDarknet53 backbone, PANet neck, and YOLOv3 detection head with a large set of training improvements — Bag of Freebies and Bag of Specials — that improve accuracy with minimal inference cost increase.

YOLOv4 achieves 43.5% AP on COCO at 65 FPS on a Tesla V100 GPU. The Darknet implementation is the original version, distinguishing it from subsequent PyTorch-based reimplementations. It remains a widely referenced detection architecture and a supported training target in Roboflow Inference.