Parameters76.0M
GFLOPs259.0
Input Size640px
Best mAP56.8%
LicenseApache-2.0
Architecture
Type
transformer
Backbone
PResNet
Neck
HybridEncoder
Head
DETR
Benchmark Results
Performance on COCO val2017 across different hardware configurations
| Hardware | Runtime | mAP@50-95 | FPS | Latency | VRAM |
|---|---|---|---|---|---|
| NVIDIA Jetson Orin Nano Super 8GB | ONNX Runtime FP32 | 56.8% | 2.9 | 343.3ms | - |
| NVIDIA Jetson Orin Nano Super 8GB | PyTorch FP32 | 56.8% | 2.8 | 358.7ms | 450 MB |
| NVIDIA Jetson Orin Nano Super 8GB | TensorRT FP16 | 56.8% | 17.3 | 57.7ms | - |
| NVIDIA Jetson Orin Nano Super 8GB | TensorRT FP32 | 56.8% | 8.5 | 118.2ms | - |
| NVIDIA RTX 5070 Ti | ONNX Runtime FP32 | 56.8% | 49.3 | 20.3ms | - |
| NVIDIA RTX 5070 Ti | PyTorch FP32 | 56.8% | 25.7 | 39.0ms | 456 MB |
| NVIDIA RTX 5070 Ti | TensorRT FP16 | 56.8% | 87.4 | 11.4ms | - |
| NVIDIA RTX 5070 Ti | TensorRT FP32 | 56.8% | 64.8 | 15.4ms | - |
Speed Breakdown(NVIDIA Jetson Orin Nano Super 8GB)
4.5ms
351.3ms
2.9ms
Preprocess
Inference
Postprocess (NMS)
Usage with LibreYOLO
from libreyolo import LibreYOLO
# Load model (auto-downloads from HuggingFace if not found locally)
model = LibreYOLO("LibreRTDETRv2r101.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,)detrnms-freePaper: 54.3% mAP