Add YOLO11 TensorRT quantization benchmark scripts

- Engine build scripts (FP16/INT8)
- Benchmark validation scripts
- Result parsing and analysis tools
- COCO dataset configuration

prepare_coco.py

@@ -0,0 +1,46 @@
+import zipfile
+import os
+from pathlib import Path
+
+def unzip_with_python(zip_path, dest_dir):
+    """使用Python解压zip文件"""
+    zip_path = Path(zip_path)
+    dest_dir = Path(dest_dir)
+    
+    print(f"Unzipping {zip_path} to {dest_dir}...")
+    
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(dest_dir)
+    
+    print(f"Done! Extracted {len(zip_ref.namelist())} files")
+
+def generate_image_list(img_dir, output_file):
+    """生成图片路径列表文件"""
+    img_dir = Path(img_dir)
+    output_file = Path(output_file)
+    
+    img_files = sorted(img_dir.glob("*.jpg"))
+    
+    print(f"Found {len(img_files)} images in {img_dir}")
+    
+    with open(output_file, 'w') as f:
+        for img_path in img_files:
+            f.write(str(img_path) + '\n')
+    
+    print(f"Generated {output_file} with {len(img_files)} entries")
+
+if __name__ == "__main__":
+    base_dir = Path(r"C:\Users\16337\PycharmProjects\Security\datasets\coco")
+    
+    # 解压 val2017.zip
+    val_zip = base_dir / "images" / "val2017.zip"
+    val_dir = base_dir / "images" / "val2017"
+    
+    if val_zip.exists() and not any(val_dir.glob("*.jpg")):
+        unzip_with_python(val_zip, base_dir / "images")
+    
+    # 生成 val2017.txt
+    generate_image_list(val_dir, base_dir / "val2017.txt")
+    
+    print("\nDone! Now you can run:")
+    print('yolo val model=yolo11n_int8_b1_8.engine data=coco.yaml imgsz=640 rect=False')