Vision Analysis
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DAMO-YOLO-Nl

damoyolo

one-stage detector with TinyNAS backbone

Parameters5.7M
GFLOPs6.0
Input Size416px
Best mAP40.5%
LicenseApache-2.0

Architecture

Type

one-stage

Backbone

TinyNAS

Neck

GiraffeNeck

Head

ZeroHead

Benchmark Results

Performance on COCO val2017 across different hardware configurations

HardwareRuntimemAP@50-95FPSLatencyVRAM
NVIDIA RTX 5070 TiPyTorch FP3240.5%19.451.6ms108 MB

Speed Breakdown(NVIDIA RTX 5070 Ti)

1.2ms
8.0ms
42.4ms
Preprocess
Inference
Postprocess (NMS)

Usage with LibreYOLO

from libreyolo import LIBREYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LIBREYOLO("libredamoyolonl.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,)
anchor-freenmsPaper: 40.5% mAP