diff --git a/ultralytics/utils/callbacks/comet.py b/ultralytics/utils/callbacks/comet.py
index 65c138bce1..224a6e0f15 100644
--- a/ultralytics/utils/callbacks/comet.py
+++ b/ultralytics/utils/callbacks/comet.py
@@ -177,7 +177,7 @@ def _format_ground_truth_annotations_for_detection(img_idx, image_path, batch, c
     return {"name": "ground_truth", "data": data}
 
 
-def _format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None):
+def _format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None, class_map=None):
     """Format YOLO predictions for object detection visualization."""
     stem = image_path.stem
     image_id = int(stem) if stem.isnumeric() else stem
@@ -187,26 +187,32 @@ def _format_prediction_annotations_for_detection(image_path, metadata, class_lab
         LOGGER.debug(f"COMET WARNING: Image: {image_path} has no bounding boxes predictions")
         return None
 
+    label_index_offset = 0
+    if class_map is not None:
+        # offset to align indices of class labels (starting from zero)
+        # with prediction's category ID indices (can start from one)
+        label_index_offset = sorted(class_map)[0]
+
     data = []
     for prediction in predictions:
         boxes = prediction["bbox"]
         score = _scale_confidence_score(prediction["score"])
         cls_label = prediction["category_id"]
         if class_label_map:
-            cls_label = str(class_label_map[cls_label])
+            cls_label = str(class_label_map[cls_label - label_index_offset])
 
         data.append({"boxes": [boxes], "label": cls_label, "score": score})
 
     return {"name": "prediction", "data": data}
 
 
-def _fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map):
+def _fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map, class_map):
     """Join the ground truth and prediction annotations if they exist."""
     ground_truth_annotations = _format_ground_truth_annotations_for_detection(
         img_idx, image_path, batch, class_label_map
     )
     prediction_annotations = _format_prediction_annotations_for_detection(
-        image_path, prediction_metadata_map, class_label_map
+        image_path, prediction_metadata_map, class_label_map, class_map
     )
 
     annotations = [
@@ -260,6 +266,7 @@ def _log_image_predictions(experiment, validator, curr_step):
     predictions_metadata_map = _create_prediction_metadata_map(jdict)
     dataloader = validator.dataloader
     class_label_map = validator.names
+    class_map = getattr(validator, "class_map", None)
 
     batch_logging_interval = _get_eval_batch_logging_interval()
     max_image_predictions = _get_max_image_predictions_to_log()
@@ -280,6 +287,7 @@ def _log_image_predictions(experiment, validator, curr_step):
                 batch,
                 predictions_metadata_map,
                 class_label_map,
+                class_map=class_map,
             )
             _log_images(
                 experiment,