Vision Analysis
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D-FINE-N

dfine

transformer detector with HGNetv2 backbone

Parameters4.0M
GFLOPs7.0
Input Size640px
Best mAP42.8%
LicenseApache-2.0

Architecture

Type

transformer

Backbone

HGNetv2

Neck

HybridEncoder

Head

FDR (Fine-grained Distribution Refinement)

Benchmark Results

Performance on COCO val2017 across different hardware configurations

HardwareRuntimemAP@50-95FPSLatencyVRAM
NVIDIA A100PyTorch FP3242.8%25.639.1ms68 MB

Speed Breakdown(NVIDIA A100)

6.1ms
21.3ms
11.6ms
Preprocess
Inference
Postprocess (NMS)

Usage with LibreYOLO

from libreyolo import LIBREYOLO

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

# 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,)
detrnms-freePaper: 42.8% mAP

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