• Corpus ID: 236088010

YOLOX: Exceeding YOLO Series in 2021

@article{Ge2021YOLOXEY,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Zheng Ge and Songtao Liu and Feng Wang and Zeming Li and Jian Sun},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.08430}
}
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 YOLONano 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… 

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References

SHOWING 1-10 OF 39 REFERENCES

YOLO9000: Better, Faster, Stronger

YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories, is introduced and a method to jointly train on object detection and classification is proposed, both novel and drawn from prior work.

PP-YOLOv2: A Practical Object Detector

A collection of existing refinements are comprehensively evaluated to improve the performance of PP-YOLO while almost keep the infer time unchanged and a significant margin of performance has been made.

YOLOv3: An Incremental Improvement

We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more

Scaled-YOLOv4: Scaling Cross Stage Partial Network

We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy.

You Only Look One-level Feature

This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization problem in object

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

This paper empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization and enable training visual recognition models on internet-scale data with high efficiency.

YOLOv4: Optimal Speed and Accuracy of Object Detection

This work uses new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, C mBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100.

You Only Look Once: Unified, Real-Time Object Detection

Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

EfficientDet: Scalable and Efficient Object Detection

This paper systematically study neural network architecture design choices for object detection and proposes a weighted bi-directional feature pyramid network (BiFPN) and a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.

Object Detection Made Simpler by Eliminating Heuristic NMS

We show a simple NMS-free, end-to-end object detection framework, of which the network is a minimal modification to a one-stage object detector such as the FCOS detection model [24]. We attain on par