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awesome-yolo-object-detection

🚀🚀🚀 A collection of some awesome public YOLO object detection series projects and the related object detection datasets.

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Created 2022-02-19 · Updated 2026-09-30 · #7905 today
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README

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 projects and datasets.

Contents

Summary

  • Famous YOLO

    • YOLOv1 (Darknet ) : "You Only Look Once: Unified, Real-Time Object Detection". (CVPR 2016)

    • YOLOv2 (Darknet ) : "YOLO9000: Better, Faster, Stronger". (CVPR 2017)

    • YOLOv3 (Darknet ) : "YOLOv3: An Incremental Improvement". (arXiv 2018)

    • YOLOv4 (WongKinYiu/PyTorch_YOLOv4 ) : "YOLOv4: Optimal Speed and Accuracy of Object Detection". (arXiv 2020)

    • Scaled-YOLOv4 (WongKinYiu/ScaledYOLOv4 ) : "Scaled-YOLOv4: Scaling Cross Stage Partial Network". (CVPR 2021)

    • YOLOv5 : YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite. docs.ultralytics.com. YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

    • YOLOv6 : "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications". (arXiv 2022).

    • YOLOv7 : "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors". (CVPR 2023).

    • YOLOv8 : NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite. docs.ultralytics.com

    • YOLOv9 : "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information". (arXiv 2024)

    • MultimediaTechLab/YOLO : YOLO: Official Implementation of YOLOv9, YOLOv7, YOLO-RD. Welcome to the official implementation of YOLOv7 and YOLOv9, YOLO-RD. This repository will contains the complete codebase, pre-trained models, and detailed instructions for training and deploying YOLOv9.

    • YOLOv10 : "YOLOv10: Real-Time End-to-End Object Detection". (arXiv 2024)

    • YOLOv11 : NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite. Ultralytics YOLOv11 s a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. docs.ultralytics.com

    • YOLOv12 : "YOLOv12: Attention-Centric Real-Time Object Detectors". (arXiv 2025)

    • YOLO-World | YOLO-World-v2 : "YOLO-World: Real-Time Open-Vocabulary Object Detection". (CVPR 2024). www.yoloworld.cc

    • YOLOE : "YOLOE: Real-Time Seeing Anything". (arXiv 2025).

  • Extensional Frameworks

    • Qwen2.5-VL : Qwen2-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. "Qwen2.5-VL Technical Report". (arXiv 2025). 2025-01-26，Qwen2.5 VL! Qwen2.5 VL! Qwen2.5 VL!. "Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution". (arXiv 2024). "Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond". (arXiv 2023).

    • Kimi-VL : Kimi-VL: Mixture-of-Experts Vision-Language Model for Multimodal Reasoning, Long-Context Understanding, and Strong Agent Capabilities. "Kimi-VL Technical Report". (arXiv 2025).

    • Visual-RFT : 🌈We introduce Visual Reinforcement Fine-tuning (Visual-RFT), the first comprehensive adaptation of Deepseek-R1's RL strategy to the multimodal field. We use the Qwen2-VL-2/7B model as our base model and design a rule-based verifiable reward, which is integrated into a GRPO-based reinforcement fine-tuning framework to enhance the performance of LVLMs across various visual perception tasks. ViRFT extends R1's reasoning capabilities to multiple visual perception tasks, including various detection tasks like Open Vocabulary Detection, Few-shot Detection, Reasoning Grounding, and Fine-grained Image Classification. "Visual-RFT: Visual Reinforcement Fine-Tuning". (arXiv 2025).

    • VLM-R1 : VLM-R1: A stable and generalizable R1-style Large Vision-Language Model. Solve Visual Understanding with Reinforced VLMs. 2025-03-20，Improving Object Detection through Reinforcement Learning with VLM-R1.

    • Florence-2 : "Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks". (CVPR 2024).

    • maestro : VLM fine-tuning for everyone. maestro is a streamlined tool to accelerate the fine-tuning of multimodal models. By encapsulating best practices from our core modules, maestro handles configuration, data loading, reproducibility, and training loop setup. It currently offers ready-to-use recipes for popular vision-language models such as Florence-2, PaliGemma 2, and Qwen2.5-VL. maestro.roboflow.com

    • Autodistill : Images to inference with no labeling (use foundation models to train supervised models). Autodistill uses big, slower foundation models to train small, faster supervised models. Using autodistill, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between. docs.autodistill.com

    • EdgeYOLO : an edge-real-time anchor-free object detector with decent performance. "Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles". (IEEE Transactions on Intelligent Transportation Systems, 2022). "EdgeYOLO: An Edge-Real-Time Object Detector". (arXiv 2023)

    • 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)

    • 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)

    • YOLO-NAS : Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS. www.supergradients.com. YOLO-NAS and YOLO-NAS-POSE architectures are out! The new YOLO-NAS delivers state-of-the-art performance with the unparalleled accuracy-speed performance, outperforming other models such as YOLOv5, YOLOv6, YOLOv7 and YOLOv8.

    • LeYOLO : "LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection". (arXiv 2024)

    • DynamicDet : "DynamicDet: A Unified Dynamic Architecture for Object Detection". (CVPR 2023)

    • DINO : "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection". (ICLR 2023).

    • GroundingDINO : "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection". (ECCV 2024).

    • RT-DETR | RT-DETRv2 : "DETRs Beat YOLOs on Real-time Object Detection". (CVPR 2024). "RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer". (arXiv 2024).

    • EasyCV : An all-in-one toolkit for computer vision. "YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6". (arXiv 2022).

    • 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)

    • 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)

    • doubleZ0108/Data-Augmentation : General Data Augmentation Algorithms for Object Detection(esp. Yolo).

  • Awesome List

  • Paper and Code Overview

    • Paper Review

    • Code Review

      • iscyy/ultralyticsPro : 🔥🔥🔥 专注于YOLO11，YOLOv8、YOLOv10、RT-DETR、YOLOv7、YOLOv5改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

      • MMDetection : OpenMMLab Detection Toolbox and Benchmark. mmdetection.readthedocs.io. (arXiv 2019)

      • MMYOLO : OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, PPYOLOE, etc. mmyolo.readthedocs.io/zh_CN/dev/

      • iscyy/yoloair : 🔥🔥🔥 专注于YOLO改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀. YOLOAir是一个基于PyTorch的YOLO算法库。统一模型代码框架、统一应用、统一改进、易于模块组合、构建更强大的网络模型。

      • iscyy/yoloair2 : ☁️💡🎈专注于改进YOLOv7，Support to improve Backbone, Neck, Head, Loss, IoU, NMS and other modules.

      • jizhishutong/YOLOU : YOLOU：United, Study and easier to Deploy. ​ The purpose of our creation of YOLOU is to better learn the algorithms of the YOLO series and pay tribute to our predecessors. YOLOv3、YOLOv4、YOLOv5、YOLOv5-Lite、YOLOv6-v1、YOLOv6-v2、YOLOv7、YOLOX、YOLOX-Lite、PP-YOLOE、PP-PicoDet-Plus、YOLO-Fastest v2、FastestDet、YOLOv5-SPD、TensorRT、NCNN、Tengine、OpenVINO. "微信公众号「集智书童」《YOLOU开源 | 汇集YOLO系列所有算法，集算法学习、科研改进、落地于一身！》"

      • WangQvQ/Yolov5_Magic : YOLO Magic🪄 is an extension based on Ultralytics' YOLOv5, designed to provide more powerful functionality and simpler operations for visual tasks.

      • positive666/yolo_research : 🚀 yolo_reserach PLUS High-level. based on yolo-high-level project (detect\pose\classify\segment):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment.

      • augmentedstartups/AS-One : Easy & Modular Computer Vision Detectors and Trackers - Run YOLO-NAS,v8,v7,v6,v5,R,X in under 20 lines of code. www.augmentedstartups.com

      • Oneflow-Inc/one-yolov5 : A more efficient yolov5 with oneflow backend 🎉🎉🎉. "微信公众号「GiantPandaCV」《One-YOLOv5 发布，一个训得更快的YOLOv5》"

      • PaddlePaddle/PaddleYOLO : 🚀🚀🚀 YOLO series of PaddlePaddle implementation, PP-YOLOE+, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, YOLOv5u, YOLOv7u, RTMDet and so on. 🚀🚀🚀

      • WangRongsheng/BestYOLO : 🌟Change the world, it will become a better place. | 以科研和竞赛为导向的最好的YOLO实践框架!

      • KangChou/Cver4s : Cver4s：Computer vision algorithm code base.

      • chaizwj/yolov8-tricks : 目标检测，采用yolov8作为基准模型，数据集采用VisDrone2019，带有自己的改进策略。

  • Learning Resources

Other Versions of YOLO

Lighter and Deployment Frameworks