YOLO11-N

yolo11Coming Soon

one-stage detector with CSPDarknet backbone

Parameters2.6M

GFLOPs6.5

Input Size640px

Best mAP38.7%

Licensenon-permissive

Architecture

Type

one-stage

Backbone

CSPDarknet

Neck

C3k2-PAFPN

Head

Decoupled

Benchmark Results

Performance on COCO val2017 across different hardware configurations

Hardware	Runtime	mAP@50-95	FPS	Latency	VRAM
NVIDIA Jetson Orin Nano Super 8GB	ONNX Runtime FP32	38.7%	8.2	121.5ms	-
NVIDIA Jetson Orin Nano Super 8GB	PyTorch FP32	38.7%	22.9	43.8ms	66 MB
NVIDIA Jetson Orin Nano Super 8GB	TensorRT FP16	38.7%	52.6	19.0ms	14 MB
Raspberry Pi 5	ncnn FP32	38.7%	12.2	81.7ms	-
Raspberry Pi 5	ONNX Runtime FP32	38.7%	6.2	162.2ms	-
Raspberry Pi 5	PyTorch FP32	38.7%	4.9	204.7ms	-

Speed Breakdown(NVIDIA Jetson Orin Nano Super 8GB)

6.4ms

99.1ms

9.9ms

Preprocess

Inference

Postprocess (NMS)

anchor-freenmsPaper: 39.5% mAP

Related Models (yolo11)

YOLO11-S YOLO11-M YOLO11-L YOLO11-X

Run any model with one line

LibreYOLO has the best catalogue of state-of-the-art detectors, all behind one MIT-licensed Python API.

from libreyolo import LibreYOLO, SAMPLE_IMAGE # LibreYOLO has the best catalogue of state-of-the-art models. model = LibreYOLO("LibreRFDETRl.pt") # RF-DETR-L (transformer flagship) results = model(SAMPLE_IMAGE, save=True) # run inference, save the annotated image # Swap in any other model, same one-line API (weights auto-download): # LibreYOLO("LibreYOLO9c.pt") # YOLO9-C # LibreYOLO("LibreYOLOXx.pt") # YOLOX-X # LibreYOLO("LibreDFINEx.pt") # D-FINE-X # LibreYOLO("LibreRTDETRr50.pt") # RT-DETR-R50