YOLOv8-M

yolov8Coming Soon

one-stage detector with CSPDarknet backbone

Parameters25.9M

GFLOPs78.9

Input Size640px

Best mAP49.4%

Licensenon-permissive

Architecture

Type

one-stage

Backbone

CSPDarknet

Neck

C2f-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	49.4%	1.4	740.5ms	-
NVIDIA Jetson Orin Nano Super 8GB	PyTorch FP32	49.4%	12.2	82.1ms	161 MB
NVIDIA Jetson Orin Nano Super 8GB	TensorRT FP16	49.4%	24.6	40.6ms	14 MB
Raspberry Pi 5	HailoRT INT8	42.4%	26.7	37.4ms	-
Raspberry Pi 5	ncnn FP32	49.4%	2.8	363.1ms	-
Raspberry Pi 5	ONNX Runtime FP32	49.4%	1.0	972.3ms	-
Raspberry Pi 5	PyTorch FP32	49.4%	1.1	906.5ms	-

Speed Breakdown(NVIDIA Jetson Orin Nano Super 8GB)

6.1ms

719.2ms

9.3ms

Preprocess

Inference

Postprocess (NMS)

anchor-freenmsPaper: 50.2% mAP

Related Models (yolov8)

YOLOv8-N YOLOv8-S YOLOv8-L YOLOv8-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