Model comparison · YOLOv9 vs YOLOv5
YOLOv9 vs YOLOv5
A head-to-head of YOLOv9 and YOLOv5: published COCO accuracy, model sizes, architecture (anchor-free, NMS-free), supported tasks and license — so you can pick the right detector for your project.
The verdict: YOLOv9 is the more accurate of the two (51.4 vs 45.4 COCO val2017 mAP50-95 @ 640px). YOLOv9 is anchor-free and YOLOv5 is anchor-based, and YOLOv5 covers 3 vision tasks to YOLOv9's 2. For a new build, YOLOv9 is the stronger pick — newer and more accurate, and more focused; reach for YOLOv5 mainly for its larger install base and tooling.
51.4 COCO val2017 mAP50-95 @ 640px on its YOLOv9m, versus 45.4 for YOLOv5.
Released 2024. Programmable Gradient Information (PGI) + GELAN.
Same inference path, so export complexity is similar.
YOLOv9 vs YOLOv5: accuracy
COCO val2017 mAP50-95 @ 640px, higher is better. The number on the right is parameter count (smaller is lighter). These are the authors' published figures — indicative of tier, not a re-run benchmark.
Smallest variant (nano tier)
Medium variant
Side-by-side specs
Architecture, tasks, sizes and license for both models. Tracking (BoT-SORT / ByteTrack) is an Ultralytics ecosystem feature available to every integrated model.
| Model | Year | Anchor-free | NMS-free | Tasks | Nano mAP | Medium mAP | License |
|---|---|---|---|---|---|---|---|
| YOLOv9 | 2024 | Yes | No | 2 (Detect, Segment) | 38.3 | 51.4 | GPL-3.0 (Ultralytics build: AGPL-3.0) |
| YOLOv5 | 2020 | No | No | 3 (Detect, Segment, Classify) | 28.0 | 45.4 | AGPL-3.0 |
The two models in depth
YOLOv9
2024 · Academia Sinica (Wang et al.)Fixes deep-network information loss with PGI + GELAN.
YOLOv9 (Academia Sinica, ECCV 2024) tackles the information bottleneck in deep networks with Programmable Gradient Information (PGI) and the Generalized ELAN (GELAN) architecture. It delivers high accuracy at low parameter counts and is integrated into Ultralytics for detection and segmentation.
- Key idea
- Programmable Gradient Information (PGI) + GELAN
- Best for
- Squeezing high accuracy out of a small parameter budget.
- Sizes
- t, s, m, c, e
- + Excellent accuracy for very few parameters
- + PGI improves training of deep models
- + Detection + segmentation via Ultralytics
- − No pose / classify / OBB
- − GPL-3.0 original repo is more restrictive
YOLOv5
2020 · UltralyticsThe classic that made YOLO easy to ship in PyTorch.
YOLOv5 made YOLO practical for everyone: pure PyTorch, easy training and export, and a huge deployment footprint that persists today. As originally released it is anchor-based and NMS-based; Ultralytics later shipped an anchor-free retrain (YOLOv5u) that adopts the YOLOv8 head and raises accuracy. The numbers here are the original anchor-based release.
- Key idea
- First fully PyTorch YOLO; CSPDarknet + PANet
- Best for
- Legacy pipelines and the widest deployment footprint.
- Sizes
- n, s, m, l, x (+ P6)
- + Enormous install base and tooling
- + Simple to train, export and deploy
- + Anchor-free YOLOv5u retrain available
- − Original release is anchor-based and lower accuracy
- − Superseded by YOLOv8/YOLO11 for new work
Other head-to-head comparisons
More matchups involving YOLOv9 and YOLOv5, each with the same spec, accuracy and architecture breakdown.
How this comparison is built
- Published numbers only. Every mAP and parameter figure is the model authors' own published value (COCO val2017 mAP50-95 @ 640px), linked from each model's card — nothing here is measured on this site.
- Indicative, not a controlled benchmark. The families use different size ladders (YOLOv9's smallest is
t, YOLOv7 has no standard medium, YOLOv5's figures are the original anchor-based release), so cross-family mAP shows the tier, not a like-for-like race. Benchmark your shortlist on your own data. - Architecture facts. Anchor-free, NMS-free, tasks and license reflect each model as officially released.
- Want a real speed test? See the YOLO tracker benchmarks for measured FPS across 10 GPUs, or book a consultation to benchmark models on your footage.
From the blog
Tutorials, code, and notes on computer vision, deep learning, and applied AI.
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Frequently asked questions
- Which YOLO model is the most accurate?
- By published COCO mAP at a comparable size, YOLO26 leads (53.1 mAP for the medium model), just ahead of YOLO11 (51.5) and YOLOv9 (51.4). All numbers here are the authors' own published figures at 640px, not a re-run benchmark, so treat small gaps as ties and test on your own data.
- What is the difference between anchor-free and NMS-free?
- Anchor-free means the model predicts boxes directly instead of refining pre-set anchor boxes (YOLOv8 onward). NMS-free (end-to-end) goes further: the model outputs final detections with no Non-Maximum-Suppression post-processing step, which simplifies export and cuts latency. YOLOv10 and YOLO26 are NMS-free; YOLOv8, YOLOv9 and YOLO11 still use NMS.
- Should I use YOLO11 or YOLO26?
- YOLO11 is the safest default today: it has the largest community, the most tutorials, and covers all five tasks. YOLO26 is newer, posts higher published accuracy, and is NMS-free and DFL-free for cleaner edge and CPU deployment. Choose YOLO11 for maximum support, YOLO26 for a fresh build that wants the newest architecture.
- Are these mAP numbers directly comparable across models?
- They are indicative, not a controlled benchmark. Every figure is the authors' own published COCO val2017 mAP at 640px, but the families don't share the same size ladder (YOLOv9's smallest is 't', YOLOv7 has no 'medium', YOLOv5's numbers are the original anchor-based release). Use them to see the general tier, then benchmark the shortlist on your own hardware and data.
- Which YOLO license can I use commercially?
- Most models here are AGPL-3.0 (Ultralytics YOLOv5, YOLOv8, YOLOv10, YOLO11, YOLO26), which requires open-sourcing derivative network-served applications unless you buy an Ultralytics Enterprise license. YOLOv7 and the original YOLOv9 repo are GPL-3.0. Always check the license against your deployment before shipping.