feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/dms_yolo/code/ultralytics/models/rtdetr/model.py

@@ -0,0 +1,63 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+"""
+Interface for Baidu's RT-DETR, a Vision Transformer-based real-time object detector.
+
+RT-DETR offers real-time performance and high accuracy, excelling in accelerated backends like CUDA with TensorRT.
+It features an efficient hybrid encoder and IoU-aware query selection for enhanced detection accuracy.
+
+References:
+    https://arxiv.org/pdf/2304.08069.pdf
+"""
+
+from ultralytics.engine.model import Model
+from ultralytics.nn.tasks import RTDETRDetectionModel
+from ultralytics.utils.torch_utils import TORCH_1_11
+
+from .predict import RTDETRPredictor
+from .train import RTDETRTrainer
+from .val import RTDETRValidator
+
+
+class RTDETR(Model):
+    """Interface for Baidu's RT-DETR model, a Vision Transformer-based real-time object detector.
+
+    This model provides real-time performance with high accuracy. It supports efficient hybrid encoding, IoU-aware query
+    selection, and adaptable inference speed.
+
+    Attributes:
+        model (str): Path to the pre-trained model.
+
+    Methods:
+        task_map: Return a task map for RT-DETR, associating tasks with corresponding Ultralytics classes.
+
+    Examples:
+        Initialize RT-DETR with a pre-trained model
+        >>> from ultralytics import RTDETR
+        >>> model = RTDETR("rtdetr-l.pt")
+        >>> results = model("image.jpg")
+    """
+
+    def __init__(self, model: str = "rtdetr-l.pt") -> None:
+        """Initialize the RT-DETR model with the given pre-trained model file.
+
+        Args:
+            model (str): Path to the pre-trained model. Supports .pt, .yaml, and .yml formats.
+        """
+        assert TORCH_1_11, "RTDETR requires torch>=1.11"
+        super().__init__(model=model, task="detect")
+
+    @property
+    def task_map(self) -> dict:
+        """Return a task map for RT-DETR, associating tasks with corresponding Ultralytics classes.
+
+        Returns:
+            (dict): A dictionary mapping task names to Ultralytics task classes for the RT-DETR model.
+        """
+        return {
+            "detect": {
+                "predictor": RTDETRPredictor,
+                "validator": RTDETRValidator,
+                "trainer": RTDETRTrainer,
+                "model": RTDETRDetectionModel,
+            }
+        }