Vision Analysis
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DAMO-YOLO-T

damoyolo

one-stage detector with TinyNAS backbone

Parameters8.5M
GFLOPs18.1
Input Size640px
Best mAP41.9%
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 FP3241.9%6.5154.5ms90 MB

Speed Breakdown(NVIDIA RTX 5070 Ti)

2.9ms
10.5ms
141.1ms
Preprocess
Inference
Postprocess (NMS)

Usage with LibreYOLO

from libreyolo import LIBREYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LIBREYOLO("libredamoyolot.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: 42% mAP