diff --git a/src/solver/det_solver.py b/src/solver/det_solver.py
index b52b9224..656f09a3 100644
--- a/src/solver/det_solver.py
+++ b/src/solver/det_solver.py
@@ -18,6 +18,24 @@
 from .det_engine import train_one_epoch, evaluate
 
 
+def prepare_eval_metric(metric, catId, type):
+    # type precision: (iou, recall, cls, area range, max dets)
+    # type recall: (iou, cls, area range, max dets)
+    if type == "precision":
+        metric = [metric[i][j][catId][0][-1] for i in range(len(metric)) for j in range(len(metric[i]))]
+    elif type == "recall":
+        metric = [metric[i][catId][0][-1] for i in range(len(metric))]
+
+    # Filter out values <= -1
+    filtered_metric = [value for value in metric if value > -1]
+
+    # Calculate mean or return NaN if empty
+    if filtered_metric:
+        return sum(filtered_metric) / len(filtered_metric)
+    else:
+        return float("nan")
+
+
 class DetSolver(BaseSolver):
 
     def fit(self, ):
@@ -98,12 +116,28 @@ def fit(self, ):
                 self.device
             )
 
+            coco_eval = coco_evaluator.coco_eval["bbox"]
+            precisions = coco_evaluator.coco_eval["bbox"].eval['precision']
+            recalls = coco_evaluator.coco_eval["bbox"].eval['recall']
+
+            class_results = {}
+            for category_id in coco_eval.cocoGt.getCatIds():
+                category_info = coco_eval.cocoGt.loadCats([category_id])[0]
+                category_name = category_info['name']
+                ap = prepare_eval_metric(precisions, category_id, "precision")
+                ar = prepare_eval_metric(recalls, category_id, "recall")
+                class_results[category_id] = {'name': category_name, 'mAP': ap, 'mAR': ar}
+
             # TODO
             for k in test_stats:
                 if self.writer and dist_utils.is_main_process():
                     for i, v in enumerate(test_stats[k]):
                         self.writer.add_scalar(f'Test/{k}_{i}'.format(k), v, epoch)
 
+                    for class_id in class_results.keys():
+                        self.writer.add_scalar(f'Test/class_{class_results[class_id]["name"]}_mAP', class_results[class_id]["mAP"], epoch)
+                        self.writer.add_scalar(f'Test/class_{class_results[class_id]["name"]}_mAR', class_results[class_id]["mAR"], epoch)
+
                 if k in best_stat:
                     best_stat['epoch'] = epoch if test_stats[k][0] > best_stat[k] else best_stat['epoch']
                     best_stat[k] = max(best_stat[k], test_stats[k][0])