YOLO26-L

yolo26Coming Soon

one-stage detector with CSPDarknet backbone

Parameters24.8M

GFLOPs86.4

Input Size640px

Best mAP53.8%

Licensenon-permissive

Architecture

Type

one-stage

Backbone

CSPDarknet

Neck

PAFPN

Head

NMS-Free End-to-End

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	53.8%	1.2	855.2ms	-
NVIDIA Jetson Orin Nano Super 8GB	PyTorch FP32	53.8%	9.3	107.2ms	222 MB
NVIDIA Jetson Orin Nano Super 8GB	TensorRT FP16	53.8%	24.3	41.2ms	11 MB
Raspberry Pi 5	ncnn FP32	53.8%	1.6	641.4ms	-
Raspberry Pi 5	ONNX Runtime FP32	53.8%	0.8	1241.6ms	-
Raspberry Pi 5	PyTorch FP32	53.8%	0.7	1377.3ms	-

Speed Breakdown(NVIDIA Jetson Orin Nano Super 8GB)

7.2ms

841.3ms

0.8ms

Preprocess

Inference

Postprocess (NMS)

anchor-freenms-freePaper: 55% mAP

Related Models (yolo26)

YOLO26-N YOLO26-S YOLO26-M YOLO26-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