Vision Analysis
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YOLOv8

yolov8

External baseline · 2023

Variants5
Parameters3.2M - 68.2M
Best Paper mAP53.9%
Licensenon-permissive

Model Variants

All variants benchmarked on COCO val2017. Click any variant for the full hardware breakdown.

ModelParamsGFLOPsPaper mAPBest FPS
YOLOv8-N3.2M8.737.3%-Details
YOLOv8-S11.2M28.644.9%-Details
YOLOv8-M25.9M78.950.2%-Details
YOLOv8-L43.7M165.252.9%-Details
YOLOv8-X68.2M257.853.9%-Details

Architecture

Type

one-stage

Backbone

CSPDarknet

Neck

C2f-PAFPN

Head

Decoupled

anchor-freenmsTrained on COCO train2017

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