ECDet

ecIn LibreYOLO

Intellindust AI Lab · arXiv 2026

Paper Code

Variants4

Parameters9.9M - 49.0M

Best Paper mAP57.9%

LicenseApache-2.0

Model Variants

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

Model	Params	GFLOPs	Paper mAP	Best FPS
ECDet-S	9.9M	26.0	51.7%	63 FPS NVIDIA RTX 5070 Ti · TensorRT FP32	Details
ECDet-M	18.0M	53.0	54.3%	59 FPS NVIDIA RTX 5070 Ti · TensorRT FP32	Details
ECDet-L	31.0M	101.0	57%	49 FPS NVIDIA RTX 5070 Ti · TensorRT FP32	Details
ECDet-X	49.0M	151.0	57.9%	46 FPS NVIDIA RTX 5070 Ti · TensorRT FP32	Details

Architecture

Type

transformer

Backbone

ECViT (compact ViT)

Neck

HybridEncoder

Head

DETR

detrnms-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