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Awesome-YOLO-Object-Detection

Awesome

🔥🔥🔥 YOLO is a great real-time one-stage object detection framework. This repository lists some awesome public YOLO object detection series projects.

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Summary

Extensional Frameworks

  • DAMO-YOLO : DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement. "DAMO-YOLO: A Report on Real-Time Object Detection Design". (arXiv 2022)

  • YOLOX-PAI : "YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6". (arXiv 2022). "微信公众号「集智书童」《YOLOX升级 | 阿里巴巴提出YOLOX-PAI,1ms内精度无敌,超越YOLOv6、PP-YOLOE》"

  • YOLOX : "YOLOX: Exceeding YOLO Series in 2021". (arXiv 2021)

  • YOLOR : "You Only Learn One Representation: Unified Network for Multiple Tasks". (arXiv 2021)

  • YOLOF : "You Only Look One-level Feature". (CVPR 2021)

  • YOLOS : "You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection". (NeurIPS 2021)

  • YOLACT & YOLACT++ : You Only Look At CoefficienTs. (ICCV 2019, IEEE TPAMI 2020)

  • Alpha-IoU : "Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression". (NeurIPS 2021)

  • CIoU : Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT). (AAAI 2020, IEEE TCYB 2021)

  • AIRDet : Welcome to AIRDet! AIRDet is an efficiency-oriented anchor-free object detector, aims to enable robust object detection in various industry scene.

  • Albumentations : Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data. "Albumentations: Fast and Flexible Image Augmentations". (Information 2020)

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🔥🔥🔥 A collection of some awesome public YOLO object detection series projects.

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