diff --git a/eval/visualize_results.py b/eval/visualize_results.py
index 64b2c3d7..1c5514f1 100644
--- a/eval/visualize_results.py
+++ b/eval/visualize_results.py
@@ -366,85 +366,80 @@ def create_performance_distribution_violin(summaries: Dict[str, Dict[str, Any]])
     return fig
 
 
-def create_performance_heatmap(summaries: Dict[str, Dict[str, Any]], categories: Dict[str, List[str]]) -> Figure:
-    """Create a heatmap of model performance across datasets.
+def create_performance_heatmap(
+    summaries: Dict[str, Dict[str, Any]],
+    categories: Dict[str, List[str]],
+) -> Figure:
+    """
+    Heat-map of model performance (0–100 %) across individual datasets.
 
-    Args:
-        summaries: Dictionary of model summaries
-        categories: Dictionary mapping categories to dataset lists
-
-    Returns:
-        Matplotlib figure
+    Rows  : models (sorted by overall mean score, high→low)
+    Cols  : datasets grouped by `categories`
+    Cell  : 100 × raw score
     """
     if not summaries:
         logger.error("No summaries provided")
         return plt.figure()
 
-    # Get all dataset names
-    all_datasets = []
-    for category, datasets in sorted(categories.items()):
-        all_datasets.extend(sorted(datasets))
+    # ---- gather dataset names in category order
+    all_datasets: List[str] = []
+    for cat, ds in sorted(categories.items()):
+        all_datasets.extend(sorted(ds))
 
-    # Sort models by overall performance
-    overall_scores = {}
-    for model_name, summary in summaries.items():
-        scores = list(summary["dataset_best_scores"].values())
-        overall_scores[model_name] = np.mean(scores)
-    models = [item[0] for item in sorted(overall_scores.items(), key=lambda x: x[1], reverse=True)]
+    # ---- sort models by overall performance
+    overall = {m: np.mean(list(s["dataset_best_scores"].values())) for m, s in summaries.items()}
+    models = [m for m, _ in sorted(overall.items(), key=lambda x: x[1], reverse=True)]
 
-    # Create score matrix
+    # ---- build score matrix (0–100)
     score_matrix = np.zeros((len(models), len(all_datasets)))
-
     for i, model in enumerate(models):
-        for j, dataset in enumerate(all_datasets):
-            score_matrix[i, j] = summaries[model]["dataset_best_scores"].get(dataset, 0)
+        for j, ds in enumerate(all_datasets):
+            score_matrix[i, j] = 100 * summaries[model]["dataset_best_scores"].get(ds, 0.0)
 
-    # Create heatmap
+    # ---- plot
     fig, ax = plt.subplots(figsize=(max(20, len(all_datasets) * 0.25), max(8, len(models) * 0.5)))
 
-    im = ax.imshow(score_matrix, cmap="viridis", aspect="auto", vmin=0, vmax=1)
+    im = ax.imshow(score_matrix, cmap="YlOrRd", aspect="auto", vmin=0, vmax=100)
 
-    # Add colorbar
-    cbar = ax.figure.colorbar(im, ax=ax)
-    cbar.ax.set_ylabel("Score", rotation=-90, va="bottom")
+    # colour-bar
+    cbar = fig.colorbar(im, ax=ax)
+    cbar.ax.set_ylabel("Score (%)", rotation=-90, va="bottom")
 
-    # Set ticks and labels
+    # ticks & labels
     ax.set_xticks(np.arange(len(all_datasets)))
+    ax.set_xticklabels(all_datasets, rotation=270, fontsize=8)
     ax.set_yticks(np.arange(len(models)))
-    ax.set_xticklabels(all_datasets, rotation=90, fontsize=8)
     ax.set_yticklabels(models)
 
-    # Add category separators and labels
-    current_idx = 0
-    for category, datasets in sorted(categories.items()):
-        if datasets:
-            # Add vertical line after each category
-            next_idx = current_idx + len(datasets)
-            if next_idx < len(all_datasets):
-                ax.axvline(x=next_idx - 0.5, color="white", linestyle="-", linewidth=2)
+    # category separators & titles
+    current = 0
+    label_offset = -0.25  # ↓ push labels down (was around −0.7)
+    for cat, ds in sorted(categories.items()):
+        if not ds:
+            continue
+        nxt = current + len(ds)
+        if nxt < len(all_datasets):
+            ax.axvline(nxt - 0.5, color="white", linewidth=2)
 
-            # Add category label
-            middle_idx = current_idx + len(datasets) / 2 - 0.5
-            ax.text(
-                middle_idx,
-                -0.5,
-                category,
-                ha="center",
-                va="top",
-                fontsize=10,
-                bbox=dict(facecolor="white", alpha=0.7, edgecolor="none"),
-            )
+        mid = current + len(ds) / 2 - 0.5
+        ax.text(
+            mid,
+            label_offset,  # <-- use offset
+            cat,
+            ha="center",
+            va="top",
+            fontsize=10,
+            bbox=dict(facecolor="white", alpha=0.7, edgecolor="none"),
+        )
+        current = nxt
 
-            current_idx = next_idx
-
-    # Add grid lines
+    # grid (mirrors comparison-plot style)
     ax.set_xticks(np.arange(-0.5, len(all_datasets), 1), minor=True)
     ax.set_yticks(np.arange(-0.5, len(models), 1), minor=True)
     ax.grid(which="minor", color="w", linestyle="-", linewidth=0.5)
 
-    plt.title("Model Performance Heatmap", size=15)
+    # ax.set_title("Model Performance by Dataset", fontsize=15)
     plt.tight_layout()
-
     return fig