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YOLOv9-S

yolov9

one-stage detector with GELAN backbone

Parameters

7.2M

FLOPs

13.5G

Input Size

640px

License

MIT

Architecture

Type

one-stage

Backbone

GELAN

Neck

PGI

Head

Decoupled

Benchmark Results
Performance on COCO val2017 across different hardware configurations
HardwareRuntimemAP@50-95FPSLatencyVRAM
NVIDIA A100PyTorch FP3238.3%28.035.7ms
Raspberry Pi 5PyTorch FP3245.4%1.3744.8ms
Speed Breakdown(Raspberry Pi 5)
End-to-end latency breakdown showing preprocessing, inference, and postprocessing times
2.9ms
734.9ms
4.0ms
Preprocess
Inference
Postprocess (NMS)
Usage with LibreYOLO
from libreyolo import LIBREYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LIBREYOLO("libreyolo9s.pt")

# Run inference
result = model("image.jpg", conf=0.25, iou=0.45)

# Process results
print(f"Found {len(result)} objects")
print(result.boxes.xyxy)   # bounding boxes (N, 4)
print(result.boxes.conf)   # confidence scores (N,)
print(result.boxes.cls)    # class IDs (N,)
balanced
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