feat(benchmark): add multi-object retrieval benchmark with SAM segmentation

mini-nav/data_loading/insdet_scenes.py

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+"""InsDet Scenes dataset for multi-object retrieval benchmark."""
+
+from pathlib import Path
+from typing import Any
+
+from benchmarks.base import BaseDataset
+from data_loading.loader import load_val_dataset
+
+
+class InsDetScenesDataset(BaseDataset):
+    """InsDet-FULL/Scenes dataset with easy/hard splits.
+
+    This dataset provides scene images with object annotations from the
+    Instance Detection (InsDet) dataset, supporting easy and hard splits.
+    """
+
+    def __init__(
+        self,
+        scenes_dir: Path | str,
+        split: str = "easy",
+    ):
+        """Initialize InsDet Scenes dataset.
+
+        Args:
+            scenes_dir: Path to the InsDet-FULL/Scenes directory.
+            split: Scene split to use ('easy' or 'hard').
+        """
+        self.scenes_dir = Path(scenes_dir)
+        self.split = split
+        self._dataset = load_val_dataset(self.scenes_dir, split)
+
+    def get_train_split(self) -> Any:
+        """Get training split (same as test for this dataset).
+
+        Returns:
+            HuggingFace Dataset for training.
+        """
+        return self._dataset
+
+    def get_test_split(self) -> Any:
+        """Get test/evaluation split.
+
+        Returns:
+            HuggingFace Dataset for testing.
+        """
+        return self._dataset
+
+    def __len__(self) -> int:
+        """Get dataset length."""
+        return len(self._dataset)
+
+    def __getitem__(self, idx: int) -> dict[str, Any]:
+        """Get a single item from the dataset.
+
+        Args:
+            idx: Index of the item.
+
+        Returns:
+            Dictionary containing:
+                - image: PIL Image
+                - image_id: Scene identifier
+                - objects: dict with bbox, category, area, id
+        """
+        return self._dataset[idx]