Vision Analysis
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PicoDet

picodetIn LibreYOLO

Baidu PaddlePaddle · arXiv 2021

Variants3
Parameters1.0M - 3.3M
Best Paper mAP40.9%
LicenseApache-2.0

Model Variants

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

ModelParamsGFLOPsPaper mAPBest FPS
PicoDet-S1.0M0.727.1%
79 FPS
NVIDIA RTX 5070 Ti · TensorRT FP16
Details
PicoDet-M2.1M2.534.3%
66 FPS
NVIDIA RTX 5070 Ti · TensorRT FP16
Details
PicoDet-L3.3M8.940.9%
40 FPS
NVIDIA RTX 5070 Ti · PyTorch FP32
Details

Architecture

Type

one-stage

Backbone

ESNet

Neck

CSP-PAN

Head

GFL

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