Model Comparisons: Choose the Best Object Detection Model for Your Project

Choosing the right neural network architecture is the cornerstone of any successful computer vision project. Welcome to the Ultralytics Model Comparison Hub! This page centralizes detailed technical analyses and performance benchmarks, dissecting the trade-offs between the latest Ultralytics YOLO11 and other leading architectures like YOLOv10, RT-DETR, and EfficientDet.

Whether your application demands the millisecond latency of edge AI or the high-fidelity precision required for medical imaging, this guide provides the data-driven insights needed to make an informed choice. We evaluate models based on mean Average Precision (mAP), inference speed, parameter efficiency, and ease of deployment.

Interactive Performance Benchmarks

Visualizing the relationship between speed and accuracy is essential for identifying the "Pareto frontier" of object detection—models that offer the best accuracy for a given speed constraint. The chart below contrasts key metrics on standard datasets like COCO.

This chart visualizes key performance metrics enabling you to quickly assess the trade-offs between different models. Understanding these metrics is fundamental to selecting a model that aligns with your specific deployment constraints.

Quick Decision Guide

Not sure where to start? Use this decision tree to narrow down the architecture that best fits your hardware and performance requirements.

graph TD
    A[Start: Define Project Needs] --> B{Deployment Hardware?}
    B -- "Edge / Mobile (CPU/NPU)" --> C{Latency Priority?}
    B -- "Cloud / GPU" --> D{Accuracy vs Speed?}

    C -- "Extreme Speed (Real-time)" --> E[YOLO11n / YOLO11s]
    C -- "Balanced Legacy" --> F[YOLOv5s / YOLOv8s]

    D -- "Max Accuracy (SOTA)" --> G[YOLO11x / RT-DETR-X]
    D -- "Balanced Performance" --> H[YOLO11m / YOLO11l]

    A --> I{Specialized Features?}
    I -- "NMS-Free Inference" --> J[YOLOv10]
    I -- "Multitask (Seg/Pose/OBB)" --> K[YOLO11 / YOLOv8]
    I -- "Video Analytics" --> L[YOLO11 + Tracking]

The Current Landscape: YOLO11 and Beyond

The field of object detection moves rapidly. While older models remain relevant for legacy support, new architectures push the boundaries of what is possible.

Ultralytics YOLO11

As the latest stable release, YOLO11 is the recommended starting point for new projects. It introduces significant architectural improvements over previous versions, including enhanced feature extraction capabilities and optimized computation graphs. It supports a full suite of tasks—detection, segmentation, pose estimation, classification, and Oriented Bounding Boxes (OBB)—within a single, unified framework.

Why Choose YOLO11?

YOLO11 represents the pinnacle of Ultralytics engineering, offering the best balance of speed and accuracy for real-world applications. It is fully supported by our ecosystem, ensuring long-term maintenance and compatibility.

Community Models: A Note on YOLO12 and YOLO13

You may encounter references to YOLO12 or YOLO13 in community discussions or repositories.

Production Caution

We currently do not recommend YOLO12 or YOLO13 for production use.

YOLO12: Utilizes attention layers that often cause training instability, excessive memory consumption, and significantly slower CPU inference speeds.
YOLO13: Benchmarks indicate only marginal accuracy gains over YOLO11 while being larger and slower. Reported results have shown issues with reproducibility.

Looking Ahead: YOLO26 and Ultralytics Platform

Ultralytics is actively developing YOLO26, targeting an open-source release in late 2025. This next-generation model aims to support all YOLO11 tasks while being smaller, faster, and natively end-to-end. Furthermore, in 2026, the Ultralytics Platform will launch as a comprehensive SaaS solution for data sourcing, auto-annotation, and cloud training, simplifying the entire MLOps lifecycle.

Watch: YOLO Models Comparison: Ultralytics YOLO11 vs. YOLOv10 vs. YOLOv9 vs. Ultralytics YOLOv8 🎉

Detailed Model Comparisons

Explore our in-depth technical comparisons to understand specific architectural differences, such as backbone selection, head design, and loss functions. We've organized them by model for easy access:

YOLO11 vs

YOLO11 builds upon the success of its predecessors with cutting-edge research. It features an improved backbone and neck architecture for better feature extraction and optimized efficiency.

YOLOv10 vs

Developed by Tsinghua University, YOLOv10 focuses on removing the Non-Maximum Suppression (NMS) step to reduce latency variance, offering state-of-the-art performance with reduced computational overhead.

YOLOv9 vs

YOLOv9 introduces Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN) to address information loss in deep neural networks.

YOLOv8 vs

Ultralytics YOLOv8 remains a highly popular choice, featuring advanced backbone and neck architectures and an anchor-free split head for optimal accuracy-speed tradeoffs.

YOLOv7 vs

YOLOv7 introduced "trainable bag-of-freebies" and model re-parameterization, focusing on optimizing the training process without increasing inference costs.

YOLOv6 vs

Meituan's YOLOv6 is designed for industrial applications, featuring Bi-directional Concatenation (BiC) modules and anchor-aided training strategies.

YOLOv5 vs

Ultralytics YOLOv5 is celebrated for its ease of use, stability, and speed. It remains a robust choice for projects requiring broad device compatibility.

RT-DETR vs

RT-DETR (Real-Time Detection Transformer) leverages vision transformers to achieve high accuracy with real-time performance, excelling in global context understanding.

PP-YOLOE+ vs

PP-YOLOE+, developed by Baidu, uses Task Alignment Learning (TAL) and a decoupled head to balance efficiency and accuracy.

DAMO-YOLO vs

From Alibaba Group, DAMO-YOLO employs Neural Architecture Search (NAS) and efficient RepGFPN to maximize accuracy on static benchmarks.

YOLOX vs

YOLOX, developed by Megvii, is an anchor-free evolution known for its decoupled head and SimOTA label assignment strategy.

EfficientDet vs

EfficientDet by Google Brain uses compound scaling and BiFPN to optimize parameter efficiency, offering a spectrum of models (D0-D7) for different constraints.

This index is continuously updated as new models are released and benchmarks are refined. We encourage you to explore these resources to find the perfect fit for your next computer vision project. If you are looking for enterprise-grade solutions with private licensing, please visit our Licensing page. Happy comparing!