Vision Analysis
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ECDet-S

ec

transformer detector with ECViT (compact ViT) backbone

Parameters9.9M
GFLOPs26.0
Input Size640px
Best mAP51.7%
LicenseApache-2.0

Architecture

Type

transformer

Backbone

ECViT (compact ViT)

Neck

HybridEncoder

Head

DETR

Benchmark Results

Performance on COCO val2017 across different hardware configurations

HardwareRuntimemAP@50-95FPSLatencyVRAM
NVIDIA RTX 5070 TiPyTorch FP3251.7%32.031.3ms102 MB

Speed Breakdown(NVIDIA RTX 5070 Ti)

8.5ms
21.9ms
0.9ms
Preprocess
Inference
Postprocess (NMS)

Usage with LibreYOLO

from libreyolo import LIBREYOLO

# Load model (auto-downloads from HuggingFace if not found locally)
model = LIBREYOLO("libreecs.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: 51.7% mAP

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