Vision Analysis
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RT-DETRv2-R101

rtdetrv2

transformer detector with PResNet backbone

Parameters76.0M
GFLOPs259.0
Input Size640px
Best mAP54.4%
LicenseApache-2.0

Architecture

Type

transformer

Backbone

PResNet

Neck

HybridEncoder

Head

DETR

Benchmark Results

Performance on COCO val2017 across different hardware configurations

HardwareRuntimemAP@50-95FPSLatencyVRAM
NVIDIA RTX 5070 TiPyTorch FP3254.4%22.444.5ms450 MB

Speed Breakdown(NVIDIA RTX 5070 Ti)

3.7ms
38.9ms
2.0ms
Preprocess
Inference
Postprocess (NMS)

Usage with LibreYOLO

from libreyolo import LIBREYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LIBREYOLO("librertdetrv2r101.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: 54.3% mAP