diff --git a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
index 5f88f2e4d..06167d20d 100644
--- a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
+++ b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
@@ -432,23 +432,142 @@ class ClusterFeedbackService {
   Future<void> debugLogClusterBlurValues(
     int clusterID, {
     int? clusterSize,
+    bool logClusterSummary = false,
+    bool logBlurValues = false,
   }) async {
-    final List<double> blurValues = await FaceMLDataDB.instance
-        .getBlurValuesForCluster(clusterID)
-        .then((value) => value.toList());
-
-    // Round the blur values to integers
-    final blurValuesIntegers =
-        blurValues.map((value) => value.round()).toList();
-
-    // Sort the blur values in ascending order
-    blurValuesIntegers.sort();
-
-    // Log the sorted blur values
+    if (!kDebugMode) return;
 
+    // Logging the clusterID
     _logger.info(
-      "Blur values for cluster $clusterID${clusterSize != null ? ' with $clusterSize photos' : ''}: $blurValuesIntegers",
+      "Debug logging for cluster $clusterID${clusterSize != null ? ' with $clusterSize photos' : ''}",
     );
+    const int biggestClusterID = 1714971868664806;
+
+    // Logging the cluster summary for the cluster
+    if (logClusterSummary) {
+      final summaryMap = await FaceMLDataDB.instance.getClusterToClusterSummary(
+        [clusterID, biggestClusterID],
+      );
+      final summary = summaryMap[clusterID];
+      if (summary != null) {
+        _logger.info(
+          "Cluster summary for cluster $clusterID says the amount of faces is: ${summary.$2}",
+        );
+      }
+
+      final biggestClusterSummary = summaryMap[biggestClusterID];
+      final clusterSummary = summaryMap[clusterID];
+      if (biggestClusterSummary != null && clusterSummary != null) {
+        _logger.info(
+          "Cluster summary for biggest cluster $biggestClusterID says the size is: ${biggestClusterSummary.$2}",
+        );
+        _logger.info(
+          "Cluster summary for current cluster $clusterID says the size is: ${clusterSummary.$2}",
+        );
+
+        // Mean distance
+        final biggestMean = Vector.fromList(
+          EVector.fromBuffer(biggestClusterSummary.$1).values,
+          dtype: DType.float32,
+        );
+        final currentMean = Vector.fromList(
+          EVector.fromBuffer(clusterSummary.$1).values,
+          dtype: DType.float32,
+        );
+        final bigClustersMeanDistance =
+            cosineDistanceSIMD(biggestMean, currentMean);
+        _logger.info(
+          "Mean distance between biggest cluster and current cluster: $bigClustersMeanDistance",
+        );
+        _logger.info(
+          'Element differences between the two means are ${biggestMean - currentMean}',
+        );
+        final currentL2Norm = currentMean.norm();
+        _logger.info(
+          'L2 norm of current mean: $currentL2Norm',
+        );
+        final trueDistance =
+            biggestMean.distanceTo(currentMean, distance: Distance.cosine);
+        _logger.info('True distance between the two means: $trueDistance');
+
+        // Median distance
+        const sampleSize = 100;
+        final Iterable<Uint8List> biggestEmbeddings = await FaceMLDataDB
+            .instance
+            .getFaceEmbeddingsForCluster(biggestClusterID);
+        final List<Uint8List> biggestSampledEmbeddingsProto =
+            _randomSampleWithoutReplacement(
+          biggestEmbeddings,
+          sampleSize,
+        );
+        final List<Vector> biggestSampledEmbeddings =
+            biggestSampledEmbeddingsProto
+                .map(
+                  (embedding) => Vector.fromList(
+                    EVector.fromBuffer(embedding).values,
+                    dtype: DType.float32,
+                  ),
+                )
+                .toList(growable: false);
+
+        final Iterable<Uint8List> currentEmbeddings =
+            await FaceMLDataDB.instance.getFaceEmbeddingsForCluster(clusterID);
+        final List<Uint8List> currentSampledEmbeddingsProto =
+            _randomSampleWithoutReplacement(
+          currentEmbeddings,
+          sampleSize,
+        );
+        final List<Vector> currentSampledEmbeddings =
+            currentSampledEmbeddingsProto
+                .map(
+                  (embedding) => Vector.fromList(
+                    EVector.fromBuffer(embedding).values,
+                    dtype: DType.float32,
+                  ),
+                )
+                .toList(growable: false);
+
+        // Calculate distances and find the median
+        final List<double> distances = [];
+        final List<double> trueDistances = [];
+        for (final biggestEmbedding in biggestSampledEmbeddings) {
+          for (final currentEmbedding in currentSampledEmbeddings) {
+            distances
+                .add(cosineDistanceSIMD(biggestEmbedding, currentEmbedding));
+            trueDistances.add(
+              biggestEmbedding.distanceTo(
+                currentEmbedding,
+                distance: Distance.cosine,
+              ),
+            );
+          }
+        }
+        distances.sort();
+        trueDistances.sort();
+        final double medianDistance = distances[distances.length ~/ 2];
+        final double trueMedianDistance =
+            trueDistances[trueDistances.length ~/ 2];
+        _logger.info(
+          "Median distance between biggest cluster and current cluster: $medianDistance (using sample of $sampleSize)",
+        );
+        _logger.info(
+          'True distance median between the two embeddings: $trueMedianDistance',
+        );
+      }
+    }
+
+    // Logging the blur values for the cluster
+    if (logBlurValues) {
+      final List<double> blurValues = await FaceMLDataDB.instance
+          .getBlurValuesForCluster(clusterID)
+          .then((value) => value.toList());
+      final blurValuesIntegers =
+          blurValues.map((value) => value.round()).toList();
+      blurValuesIntegers.sort();
+      _logger.info(
+        "Blur values for cluster $clusterID${clusterSize != null ? ' with $clusterSize photos' : ''}: $blurValuesIntegers",
+      );
+    }
 
     return;
   }