diff --git a/src/training/plot_metrics.py b/src/training/plot_metrics.py
index 37c45ae..df97c62 100644
--- a/src/training/plot_metrics.py
+++ b/src/training/plot_metrics.py
@@ -70,9 +70,9 @@ def plot_train_metrics(train_df: pd.DataFrame, out_dir: Path) -> None:
 
 
 def plot_val_metrics(val_df: pd.DataFrame, out_dir: Path, train_recall_df: pd.DataFrame | None = None) -> None:
-    """Plot recall metrics: train vs val R@K."""
-    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
-    fig.suptitle("Recall Metrics (train vs val)", fontsize=16, fontweight="bold")
+    """Plot recall + AP metrics: train vs val."""
+    fig, axes = plt.subplots(1, 3, figsize=(20, 5))
+    fig.suptitle("Retrieval Metrics (train vs val)", fontsize=16, fontweight="bold")
 
     # 1. Recall@K (q→g): train + val.
     ax = axes[0]
@@ -102,6 +102,23 @@ def plot_val_metrics(val_df: pd.DataFrame, out_dir: Path, train_recall_df: pd.Da
     ax.set_ylim(0, 1)
     ax.legend(fontsize=8)
 
+    # 3. AP (train vs val, both directions).
+    ax = axes[2]
+    if "ap_q2g" in val_df.columns:
+        sns.lineplot(data=val_df, x="epoch", y="ap_q2g", ax=ax, marker="o", linewidth=2, color="royalblue", label="val AP q→g")
+    if "ap_g2q" in val_df.columns:
+        sns.lineplot(data=val_df, x="epoch", y="ap_g2q", ax=ax, marker="s", linewidth=2, color="coral", label="val AP g→q")
+    if train_recall_df is not None:
+        if "ap_q2g" in train_recall_df.columns:
+            sns.lineplot(data=train_recall_df, x="epoch", y="ap_q2g", ax=ax, marker="x", linewidth=1.5, linestyle="--", color="royalblue", label="train AP q→g")
+        if "ap_g2q" in train_recall_df.columns:
+            sns.lineplot(data=train_recall_df, x="epoch", y="ap_g2q", ax=ax, marker="x", linewidth=1.5, linestyle="--", color="coral", label="train AP g→q")
+    ax.set_title("Average Precision")
+    ax.set_xlabel("Epoch")
+    ax.set_ylabel("AP")
+    ax.set_ylim(0, 1)
+    ax.legend(fontsize=8)
+
     plt.tight_layout()
     path = out_dir / "val_metrics.png"
     fig.savefig(path, dpi=150, bbox_inches="tight")
diff --git a/src/training/train_gtauav.py b/src/training/train_gtauav.py
index f4f8358..7950bf9 100644
--- a/src/training/train_gtauav.py
+++ b/src/training/train_gtauav.py
@@ -228,18 +228,27 @@ def _evaluate(
     if batch_losses:
         metrics["loss"] = sum(batch_losses) / len(batch_losses)
 
+    # R@K and AP (q→g).
     sorted_idx = sim.argsort(dim=1, descending=True)
     for k in k_values:
         top_k = sorted_idx[:, :k]
         hit = (top_k == targets.unsqueeze(1)).any(dim=1).float()
         metrics[f"r@{k}_q2g"] = float(hit.mean().item())
 
+    # AP q→g: for each query, rank of the correct gallery = 1/(rank+1).
+    ranks_q2g = (sorted_idx == targets.unsqueeze(1)).nonzero(as_tuple=True)[1].float()
+    metrics["ap_q2g"] = float((1.0 / (ranks_q2g + 1)).mean().item())
+
+    # R@K and AP (g→q).
     sorted_idx_g2q = sim.t().argsort(dim=1, descending=True)
     for k in k_values:
         top_k = sorted_idx_g2q[:, :k]
         hit = (top_k == targets.unsqueeze(1)).any(dim=1).float()
         metrics[f"r@{k}_g2q"] = float(hit.mean().item())
 
+    ranks_g2q = (sorted_idx_g2q == targets.unsqueeze(1)).nonzero(as_tuple=True)[1].float()
+    metrics["ap_g2q"] = float((1.0 / (ranks_g2q + 1)).mean().item())
+
     metrics["gate_q"] = model.fusion_query.gate_value
     metrics["gate_g"] = model.fusion_gallery.gate_value
     return metrics
@@ -696,11 +705,13 @@ def train(cfg: TrainConfigGTAUAV) -> None:
             csv_logger.log_train_recall(epoch, train_recall)
             tracker.log_train(epoch, {f"recall/{k}": v for k, v in train_recall.items() if k.startswith("r@")}, step=global_step)
             LOGGER.info(
-                "train-recall epoch=%d  R@1=%.4f  R@5=%.4f  R@10=%.4f",
+                "train-recall epoch=%d  R@1=%.4f  R@5=%.4f  R@10=%.4f  AP=%.4f  loss=%.4f",
                 epoch,
                 train_recall.get("r@1_q2g", 0.0),
                 train_recall.get("r@5_q2g", 0.0),
                 train_recall.get("r@10_q2g", 0.0),
+                train_recall.get("ap_q2g", 0.0),
+                train_recall.get("loss", 0.0),
             )
 
             # Val R@K (full test set).
@@ -721,13 +732,14 @@ def train(cfg: TrainConfigGTAUAV) -> None:
                 tracker.log_scalar("val/best_r@1_q2g", best_r1, step=global_step)
 
             LOGGER.info(
-                "val epoch=%d  R@1=%.4f  R@5=%.4f  R@10=%.4f  gate_q=%.4f  gate_g=%.4f",
+                "val epoch=%d  R@1=%.4f  R@5=%.4f  R@10=%.4f  AP=%.4f  loss=%.4f  gate_q=%.4f",
                 epoch,
                 val_metrics.get("r@1_q2g", 0.0),
                 val_metrics.get("r@5_q2g", 0.0),
                 val_metrics.get("r@10_q2g", 0.0),
+                val_metrics.get("ap_q2g", 0.0),
+                val_metrics.get("loss", 0.0),
                 val_metrics.get("gate_q", 1.0),
-                val_metrics.get("gate_g", 1.0),
             )
 
             # --- Grad-CAM visualization ---