YOLO11-S

yolo11Coming Soon

one-stage detector with CSPDarknet backbone

Parameters9.4M

GFLOPs21.5

Input Size640px

Best mAP45.7%

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	45.7%	3.8	265.9ms	-
NVIDIA Jetson Orin Nano Super 8GB	PyTorch FP32	45.7%	20.7	48.3ms	110 MB
NVIDIA Jetson Orin Nano Super 8GB	TensorRT FP16	45.7%	41.3	24.2ms	14 MB
Raspberry Pi 5	ncnn FP32	45.7%	5.7	174.5ms	-
Raspberry Pi 5	ONNX Runtime FP32	45.7%	2.6	378.1ms	-
Raspberry Pi 5	PyTorch FP32	45.7%	2.3	435.8ms	-

Speed Breakdown(NVIDIA Jetson Orin Nano Super 8GB)

6.1ms

244.5ms

9.3ms

Preprocess

Inference

Postprocess (NMS)

anchor-freenmsPaper: 47% mAP

Related Models (yolo11)

YOLO11-N 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