first commit

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+*.egg-info/
+.eggs/
+dist/
+build/
+*.egg
+.venv/
+venv/
+env/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+desktop.ini
+
+# Model weights & exports
+# weights/
+# *.pt
+# *.pth
+# *.onnx
+# *.engine
+# *.weights
+
+# Images (inference input/output)
+*_marked.jpg
+*_marked.jpeg
+*_marked.png
+image.jpg
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.webp
+!image1.jpg
+!image2.jpg
+!image3.jpg
+
+# Ultralytics outputs
+runs/
+output/
+outputs/
+results/
+
+# ClearML
+clearml.conf
+*.log
+
+# Jupyter
+.ipynb_checkpoints/
+
+# Environment & secrets
+.env
+.env.*
+*.pem

requirements.txt

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+ultralytics==8.4.71
+requests==2.32.5

start.py

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+import json
+import math
+from pathlib import Path
+from ultralytics import YOLO
+import requests
+import argparse
+
+from clearml import Task  # 1. ClearML 임포트
+
+# 방법 A) 가장 최신 트렌드인 NMS-Free 임베디드 특화 모델 로드
+model = YOLO("./weights/yolo26m.pt")  # Medium 크기 가중치 자동 다운로드
+
+task = Task.init(
+    project_name="Normal_Object_Detection", 
+    task_name="YOLOv26m_coc80_Inference_Agent"
+)
+
+def xyxy_to_xywh(box: dict) -> dict:
+    x1 = math.floor(box["x1"])
+    y1 = math.floor(box["y1"])
+    x2 = math.ceil(box["x2"])
+    y2 = math.ceil(box["y2"])
+    return {"x": x1, "y": y1, "w": x2 - x1, "h": y2 - y1}
+
+
+# 방법 B) 복잡한 구도나 정밀 식별에 강한 어텐션 기반 모델 로드
+# model = YOLO("yolo12m.pt")
+
+def main(image_path: str):
+    # 이미지 원격 추론 테스트 (COCO 80종 기본 탐지 가능)
+    # results = model("https://acai.ketidev.kr:20443/detect/image/202606/20260619_145116_image.jpg")
+    results = model(image_path)
+
+    result = results[0]
+
+    image_path = Path(result.path)
+    marked_path = image_path.with_name(f"{image_path.stem}_marked{image_path.suffix}")
+    result.save(filename=str(marked_path))
+
+    detections = []
+    for det in result.summary():
+        detections.append({**det, "box": xyxy_to_xywh(det["box"])})
+
+    output = {
+        "path": result.path,
+        "marked_path": str(marked_path),
+        "shape": {"height": result.orig_shape[0], "width": result.orig_shape[1]},
+        "speed_ms": result.speed,
+        "detections": detections,
+    }
+    print(json.dumps(output, indent=2, ensure_ascii=False))
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--image_url", type=str)
+    args = parser.parse_args()
+
+    image_url = args.image_url
+
+    if image_url is None:
+        print("Image path is required")
+        exit(1)
+
+    response = requests.get(image_url)
+    response.raise_for_status()
+    image_data = response.content
+    with open("image.jpg", "wb") as f:
+        f.write(image_data)
+    image_path = "image.jpg"
+
+    main(image_path)