Vision Analysis
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RF-DETR-S

rfdetr

transformer detector with DINOv2 backbone

PaperCode
Parameters32.1M
GFLOPs0.0
Input Size512px
Best mAP55.3%
LicenseApache-2.0

Architecture

Type

transformer

Backbone

DINOv2

Neck

Agg2Former

Head

DETR

Benchmark Results

Performance on COCO val2017 across different hardware configurations

HardwareRuntimeDatasetCredit / sourcemAP@50-95FPSLatencyGPU allocation
NVIDIA DGX SparkPyTorch FP32mini500 (500 images)

Submitted by @DavidDiazMerino

Source PRRun JSON

55.1%59.916.7ms199 MB
NVIDIA Jetson Orin Nano Super 8GBONNX Runtime FP32mini500 (500 images)

Run JSON

55.1%6.2161.5ms-
NVIDIA Jetson Orin Nano Super 8GBPyTorch FP32mini500 (500 images)

Run JSON

55.1%6.9144.6ms175 MB
NVIDIA Jetson Orin Nano Super 8GBTensorRT FP16mini500 (500 images)

Run JSON

55.2%15.564.4ms-
NVIDIA Jetson Orin Nano Super 8GBTensorRT FP32mini500 (500 images)

Run JSON

55.1%14.569.1ms-
NVIDIA RTX 5070 TiONNX Runtime FP32mini500 (500 images)

Run JSON

55.1%68.514.6ms-
NVIDIA RTX 5070 TiPyTorch FP32mini500 (500 images)

Run JSON

55.1%35.827.9ms176 MB
NVIDIA RTX 5070 TiTensorRT FP16mini500 (500 images)

Run JSON

55.3%71.614.0ms-
NVIDIA RTX 5070 TiTensorRT FP32mini500 (500 images)

Run JSON

55.1%77.512.9ms-
Raspberry Pi 5ONNX Runtime FP32mini500 (500 images)

Run JSON

55.1%1.2850.1ms-
Raspberry Pi 5PyTorch FP32mini500 (500 images)

Run JSON

55.1%1.0959.4ms-

Speed Breakdown(NVIDIA DGX Spark)

3.3ms
12.4ms
1.0ms
Preprocess
Inference
Postprocess (NMS)

Usage with LibreYOLO

from libreyolo import LibreYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LibreYOLO("LibreRFDETRs.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: 53% mAP

Related Models (rfdetr)

RF-DETR-NRF-DETR-MRF-DETR-LRF-DETR-Seg-NRF-DETR-Seg-SRF-DETR-Seg-MRF-DETR-Seg-L

Run any model with one line

LibreYOLO has the best catalogue of state-of-the-art detectors, all behind one MIT-licensed Python API.

from libreyolo import LibreYOLO, SAMPLE_IMAGE

# LibreYOLO has the best catalogue of state-of-the-art models.
model = LibreYOLO("LibreRFDETRl.pt")           # RF-DETR-L (transformer flagship)
results = model(SAMPLE_IMAGE, save=True)        # run inference, save the annotated image

# Swap in any other model, same one-line API (weights auto-download):
#   LibreYOLO("LibreYOLO9c.pt")      # YOLO9-C
#   LibreYOLO("LibreYOLOXx.pt")      # YOLOX-X
#   LibreYOLO("LibreDFINEx.pt")      # D-FINE-X
#   LibreYOLO("LibreRTDETRr50.pt")   # RT-DETR-R50