Vision Analysis
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YOLOX-L

yolox

one-stage detector with CSPDarknet backbone

Parameters54.2M
GFLOPs78.0
Input Size640px
Best mAP48.3%
LicenseApache-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 FP3248.3%37.127.0ms307 MB

Speed Breakdown(NVIDIA A100)

6.0ms
14.3ms
6.7ms
Preprocess
Inference
Postprocess (NMS)

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,)
anchor-freenmsPaper: 49.7% mAP