Model comparison · YOLO11 vs YOLOv5 (v5u)

YOLO11 vs YOLOv5 (v5u)

A size-tier-by-size-tier comparison of Ultralytics YOLO11 and Ultralytics YOLOv5 (v5u): published COCO accuracy, parameter counts, licensing and which one to reach for. Every figure is the model authors' own, linked to its source.

CompareYOLO11
AgainstYOLOv5 (v5u)
74%fewer params

mAP comparison

YOLO11YOLOv5 (v5u)
31.037.844.551.358.0mAPNanoSmallMediumLargeExtra largeModel size tierYOLO11n: 39.5 mAP, 2.6M paramsYOLO11s: 47.0 mAP, 9.4M paramsYOLO11m: 51.5 mAP, 20.1M paramsYOLO11l: 53.4 mAP, 25.3M paramsYOLO11x: 54.7 mAP, 56.9M paramsYOLOv5nu: 34.3 mAP, 2.6M paramsYOLOv5su: 43.0 mAP, 9.1M paramsYOLOv5mu: 49.0 mAP, 25.1M paramsYOLOv5lu: 52.2 mAP, 53.2M paramsYOLOv5xu: 53.2 mAP, 97.2M params

YOLO11

  • Nano 39.5
  • Small 47.0
  • Medium 51.5
  • Large 53.4
  • Extra large 54.7

YOLOv5 (v5u)

  • Nano 34.3
  • Small 43.0
  • Medium 49.0
  • Large 52.2
  • Extra large 53.2
COCO mAP at 640px, as published by each model’s authors. The y axis spans 31.0 to 58.0 rather than starting at zero, so small differences stay visible. Points are the published checkpoints; the joining lines are for reading order only.

Accuracy and size, tier by tier

Each row pairs the equivalent rung of both size ladders, so the numbers sit side by side at comparable capacity. Every figure is the model authors’ own.

  • COCO mAP50-95 @ 640px
  • mAP: higher is better
  • Params (M): lower is better
YOLO11 and YOLOv5 (v5u) compared at each size tier: published COCO mAP50-95 at 640px and parameter counts in millions.
TierYOLO11YOLOv5 (v5u)Δ mAP
ModelmAPParamsModelmAPParams
NanoYOLO11n39.52.6MYOLOv5nu34.32.6M+5.2
SmallYOLO11s47.09.4MYOLOv5su43.09.1M+4.0
MediumYOLO11m51.520.1MYOLOv5mu49.025.1M+2.5
LargeYOLO11l53.425.3MYOLOv5lu52.253.2M+1.2
Extra largeYOLO11x54.756.9MYOLOv5xu53.297.2M+1.5

Δ mAP is YOLO11 minus YOLOv5 (v5u): positive means YOLO11 is ahead at that tier. Sources: YOLO11 docs · YOLOv5 (v5u) docs

Additional information

  • YOLO11 posts the higher published COCO mAP at all 5 matched size tiers, averaging 2.88 mAP ahead of YOLOv5 (v5u). The gap is widest at the nano tier, where YOLO11 leads by 5.2 mAP. At the top of each range, YOLO11 reaches 54.7 mAP against 53.2.

  • 74% fewer parameters, same accuracy. YOLO11l reaches 53.4 mAP from 25.3M parameters, matching or beating YOLOv5xu at 53.2 mAP from 97.2M.

    Parameter count is not latency. It is a good proxy for download size and memory, but throughput depends on your hardware, batch size and export format, so measure on your own target.

YOLO11

The best-supported all-round default across five tasks.

Released
2024 · Ultralytics
License
AGPL-3.0
Framework
Ultralytics (PyTorch)
Tasks
Detect, Segment, Classify, Pose, OBB

An anchor-free, single-pass detector that predicts objects in one shot across five vision tasks, giving a strong balance of speed and accuracy for everyday production use.

Download YOLO11 weights →

YOLOv5 (v5u)

The classic that made YOLO easy to ship, retrained anchor-free.

Released
2020 · Ultralytics
License
AGPL-3.0
Framework
Ultralytics (PyTorch)
Tasks
Detect, Segment, Classify

The anchor-free v5u retrain of the classic PyTorch YOLO, keeping its easy training and export while adopting the newer YOLOv8 detection head for better accuracy.

Download YOLOv5 (v5u) weights →

YOLO11 and YOLOv5 (v5u) against the whole lineage

YOLO11 and YOLOv5 (v5u) against every other detector here, on one shared scale. The rest are dimmed; hover any row to bring it back.

Every YOLO detector in this section, with release year, publisher, published COCO mAP50-95 range at 640px, parameter-count range and licence.
ModelReleasedmAP rangeParams rangeLicence
YOLO26202640.9 to 57.52.4M to 55.7MAGPL-3.0
YOLO12202540.6 to 55.22.6M to 59.1MAGPL-3.0
YOLO11202439.5 to 54.72.6M to 56.9MAGPL-3.0
YOLOv10202438.5 to 54.42.3M to 29.5MAGPL-3.0
YOLOv9202438.3 to 55.62.0M to 58.1MGPL-3.0
YOLOv8202337.3 to 53.93.2M to 68.2MAGPL-3.0
YOLOv5 (v5u)202034.3 to 53.22.6M to 97.2MAGPL-3.0