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YOLOX-L

yolox

one-stage detector with CSPDarknet backbone

Parameters

54.2M

FLOPs

78.0G

Input Size

640px

License

Apache-2.0

Architecture

Type

one-stage

Backbone

CSPDarknet

Neck

PAFPN

Head

Decoupled

Benchmark Results
Performance on COCO val2017 across different hardware configurations
HardwareRuntimemAP@50-95FPSLatencyVRAM
NVIDIA A100PyTorch FP3244.2%39.925.1ms
Speed Breakdown
End-to-end latency breakdown showing preprocessing, inference, and postprocessing times

No detailed timing data available

Usage with LibreYOLO
from libreyolo import LIBREYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LIBREYOLO("libreyoloXl.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,)
high-accuracymegvii