diff --git a/src/encord_active/public/active_project.py b/src/encord_active/public/active_project.py
index 753e63278..ea9787224 100644
--- a/src/encord_active/public/active_project.py
+++ b/src/encord_active/public/active_project.py
@@ -6,7 +6,7 @@
 
 import pandas as pd
 from encord.objects import OntologyStructure
-from sqlalchemy import Integer
+from sqlalchemy import Integer, func
 from sqlalchemy.engine import Engine
 from sqlmodel import Session, select
 
@@ -79,7 +79,53 @@ def __init__(self, engine: Optional[Engine], project_name: str, root_path: Optio
     def get_ontology(self) -> OntologyStructure:
         return self.ontology
 
-    def get_prediction_metrics(self, include_data_uris: bool = False) -> pd.DataFrame:
+    def _join_path_statement(self, stmt, base_model: AnalyticsModel):
+        stmt = stmt.add_columns(ProjectDataUnitMetadata.data_uri, ProjectDataUnitMetadata.data_uri_is_video)
+        stmt = stmt.join(
+            ProjectDataUnitMetadata,
+            onclause=(base_model.du_hash == ProjectDataUnitMetadata.du_hash)
+            & (base_model.frame == ProjectDataUnitMetadata.frame),
+        ).where(ProjectDataUnitMetadata.project_hash == self.project_hash)
+
+        def transform(df):
+            if self._root_path is None:
+                raise ValueError(f"Root path is not set. Provide it in the constructor or use `from_db_file`")
+            df["data_uri"] = df.data_uri.map(partial(url_to_file_path, project_dir=self._root_path))
+            return df
+
+        return stmt, transform
+
+    def _join_data_tags_statement(self, stmt, base_model: AnalyticsModel, group_by_cols=None):
+        stmt = stmt.add_columns(("[" + func.group_concat('"' + ProjectTag.name + '"', ", ") + "]").label("data_tags"))
+        stmt = (
+            stmt.join(
+                ProjectTaggedDataUnit,
+                onclause=(base_model.du_hash == ProjectTaggedDataUnit.du_hash)
+                & (base_model.frame == ProjectTaggedDataUnit.frame),
+            )
+            .join(ProjectTag, onclause=ProjectTag.tag_hash == ProjectTaggedDataUnit.tag_hash)
+            .where(
+                ProjectTag.project_hash == self.project_hash, ProjectTaggedDataUnit.project_hash == self.project_hash
+            )
+            .group_by(base_model.du_hash, base_model.frame, *(group_by_cols or []))
+        )
+
+        def transform(df):
+            df["data_tags"] = df.data_tags.map(eval)
+            return df
+
+        return stmt, transform
+
+    def get_prediction_metrics(self, include_data_uris: bool = False, include_data_tags: bool = False) -> pd.DataFrame:
+        """
+        Returns a pandas data frame with all the prediction metrics.
+
+        Args:
+            include_data_uris: If set to true, the data frame will contain a data_uri column containing the path to the image file.
+            include_data_tags: If set to true, the data frame will contain a data_tags column containing a list of tags for the underlying image.
+                Disclaimer. We take no measures here to counteract SQL injections so avoid using "funky" characters like '"\\/` in your tag names.
+
+        """
         if self._model_hash is None:
             raise ValueError(f"Project with name {self._project_name} does not have any model predictions")
 
@@ -102,47 +148,47 @@ def get_prediction_metrics(self, include_data_uris: bool = False) -> pd.DataFram
                 (P.feature_hash == P.match_feature_hash).cast(Integer).label("true_positive"),  # type: ignore
             ).where(P.project_hash == self.project_hash, P.prediction_hash == self._model_hash)
 
-            transform = None
+            transforms = []
             if include_data_uris:
                 stmt, transform = self._join_path_statement(stmt, P)
+                transforms.append(transform)
+            if include_data_tags:
+                stmt, transform = self._join_data_tags_statement(stmt, P, group_by_cols=[P.object_hash])
+                transforms.append(transform)
 
             df = pd.DataFrame(sess.exec(stmt).all())
             df.columns = list(sess.exec(stmt).keys())
 
-            if transform is not None:
+            for transform in transforms:
                 df = transform(df)
 
         return df
 
-    def _join_path_statement(self, stmt, base_model: AnalyticsModel):
-        stmt = stmt.add_columns(ProjectDataUnitMetadata.data_uri, ProjectDataUnitMetadata.data_uri_is_video)
-        stmt = stmt.join(
-            ProjectDataUnitMetadata,
-            onclause=(base_model.du_hash == ProjectDataUnitMetadata.du_hash)
-            & (base_model.frame == ProjectDataUnitMetadata.frame),
-        ).where(ProjectDataUnitMetadata.project_hash == self.project_hash)
-
-        def transform(df):
-            if self._root_path is None:
-                raise ValueError(f"Root path is not set. Provide it in the constructor or use `from_db_file`")
-            df["data_uri"] = df.data_uri.map(partial(url_to_file_path, project_dir=self._root_path))
-            return df
-
-        return stmt, transform
+    def get_images_metrics(self, *, include_data_uris: bool = False, include_data_tags: bool = True) -> pd.DataFrame:
+        """
+        Returns a pandas data frame with all the prediction metrics.
 
-    def get_images_metrics(self, *, include_data_uris: bool = False) -> pd.DataFrame:
+        Args:
+            include_data_uris: If set to true, the data frame will contain a data_uri column containing the path to the image file.
+            include_data_tags: If set to true, the data frame will contain a data_tags column containing a list of tags for the underlying image.
+                Disclaimer. We take no measures here to counteract SQL injections so avoid using "funky" characters like '"\\/` in your tag names.
+        """
         with Session(self._engine) as sess:
             stmt = select(*[c for c in ProjectDataAnalytics.__table__.c]).where(  # type: ignore -- hack to get all columns without the pydantic model
                 ProjectDataAnalytics.project_hash == self.project_hash
             )
-            transform = None
+            transforms = []
             if include_data_uris:
                 stmt, transform = self._join_path_statement(stmt, ProjectDataAnalytics)
+                transforms.append(transform)
+            if include_data_tags:
+                stmt, transform = self._join_data_tags_statement(stmt, ProjectDataAnalytics)
+                transforms.append(transform)
             image_metrics = sess.exec(stmt).all()
             df = pd.DataFrame(image_metrics)
             df.columns = list(sess.execute(stmt).keys())
 
-            if transform is not None:
+            for transform in transforms:
                 df = transform(df)
 
         return df