Lillthorin/YOLOlite_edge_s_320_p2

YOLOLite-edge_s (ONNX, 320×320, P2 head)

YOLOLite-edge_s is a lightweight, CPU-focused object detection model designed for extreme edge performance at very low resolutions.
This P2-enabled variant is optimized for small-object detection at 320×320 while maintaining high real-time throughput on ordinary CPUs.

📦 Full source code: https://github.com/Lillthorin/YoloLite-Official-Repo
📊 Full Benchmark Results: See BENCHMARK.md in the repository

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🔍 Key Features

• Real-time CPU throughput: 94–101 FPS end-to-end
• Fast ONNX inference: 8–10 ms per frame
• Optimized for industrial, robotics, and edge computing
• Enhanced P2 head for small-object performance at 320px
• Supports resize or letterbox preprocessing
• Evaluated across 40+ diverse Roboflow100 datasets

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⚡ Real-World CPU Performance (ONNX Runtime)

Tested on 1080p traffic footage (intersection.mp4) using
onnx_intersection_showcase.py with:

• Model: edge_s_320_p2.onnx
• Execution Provider: CPUExecutionProvider
• Preprocessing: Resize
• Resolution: 320×320

Measurement	Result
End-to-end FPS	94–101 FPS
Raw inference latency	8–10 ms per frame
Pipeline includes	video → resize → inference → NMS → drawing

These values represent actual full-pipeline performance, not isolated model latency.

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🧪 Example Usage

from infer_onnx import ONNX_Predict
import cv2

predict = ONNX_Predict(
    "edge_s_320_p2.onnx",
    providers=["CPUExecutionProvider"],
    use_letterbox=False
)

frame = cv2.imread("image.jpg")
boxes, scores, classes = predict.infer_image(frame, img_size=320)

for (x1, y1, x2, y2), score, cls in zip(boxes, scores, classes):
    print(x1, y1, x2, y2, score, cls)