Vision Analysis
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YOLOv10

yolov10

Tsinghua University · NeurIPS 2024

Variants5
Parameters2.3M - 29.5M
Best Paper mAP54.4%
Licensenon-permissive

Model Variants

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

ModelParamsGFLOPsPaper mAPBest FPS
YOLOv10-N2.3M6.738.5%-Details
YOLOv10-S7.2M21.646.3%-Details
YOLOv10-M15.4M59.151.1%-Details
YOLOv10-L24.4M120.353.2%-Details
YOLOv10-X29.5M160.454.4%-Details

Architecture

Type

one-stage

Backbone

CSPDarknet

Neck

PAFPN

Head

NMS-Free Dual Head

anchor-freenms-freeTrained 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