NERVE is a new 600GB multi-sensor dataset with DVS, RGB-D, and 24/77GHz radar plus baselines showing DVS+77GHz radar fusion improves human detection to 47.5% mAP with sub-1.8m distance error.
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YOLOX: Exceeding YOLO Series in 2021
Canonical reference. 70% of citing Pith papers cite this work as background.
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70% of classified citations
abstract
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Further, we won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model. We hope this report can provide useful experience for developers and researchers in practical scenes, and we also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported. Source code is at https://github.com/Megvii-BaseDetection/YOLOX.
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representative citing papers
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A framework with new metrics and train-time/post-hoc calibrators aligns probabilistic object detectors to annotator disagreement distributions for classification and localization without ground truth.
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A deterministic queue-based matching algorithm using geometric overlaps and virtual lane discretization enables 99.8% handover success rate for continuous identity persistence in multi-UAV vehicle tracking.
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CalibFree enables calibration-free multi-camera tracking via self-supervised feature separation through single-view distillation and cross-view reconstruction, reporting 3% higher accuracy and 7.5% better F1 on tested datasets.
FUN is an end-to-end Focal U-Net that performs joint hyperspectral image reconstruction and object detection via multi-task learning with focal modulation, achieving SOTA results with 40% fewer parameters and a new 363-image dataset.
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Scale-Gest creates a runtime-selectable family of tiny-YOLO models with device-calibrated ACE profiles and an ROI gate that cuts per-frame energy by 4x while holding event-level F1 at 0.8-0.9 on a new driving-gesture dataset.
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Dual-head knowledge distillation partitions the linear classifier into separate heads for logit and probability losses to exploit logits without causing classification head collapse.
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