diff --git a/mmocr/metrics/hmean_iou_metric.py b/mmocr/metrics/hmean_iou_metric.py
index 4a8f6f93..ea95831c 100644
--- a/mmocr/metrics/hmean_iou_metric.py
+++ b/mmocr/metrics/hmean_iou_metric.py
@@ -171,15 +171,15 @@ class HmeanIOUMetric(BaseMetric):
                 dataset_pred_num[i] += np.sum(~pred_ignore_flags)
 
         for i, pred_score_thr in enumerate(self.pred_score_thrs):
-            precision, recall, hmean = compute_hmean(
+            recall, precision, hmean = compute_hmean(
                 int(dataset_hit_num[i]), int(dataset_hit_num[i]),
                 int(dataset_gt_num), int(dataset_pred_num[i]))
             eval_results = dict(
                 precision=precision, recall=recall, hmean=hmean)
-            logger.info(f'prediction score threshold: {pred_score_thr}, '
-                        f'recall: {eval_results["recall"]:.3f}, '
-                        f'precision: {eval_results["precision"]:.3f}, '
-                        f'hmean: {eval_results["hmean"]:.3f}\n')
+            logger.info(f'prediction score threshold: {pred_score_thr:.2f}, '
+                        f'recall: {eval_results["recall"]:.4f}, '
+                        f'precision: {eval_results["precision"]:.4f}, '
+                        f'hmean: {eval_results["hmean"]:.4f}\n')
             if eval_results['hmean'] > best_eval_results['hmean']:
                 best_eval_results = eval_results
         return best_eval_results
diff --git a/tests/test_metrics/test_hmean_iou.py b/tests/test_metrics/test_hmean_iou.py
index b2d53d21..75952147 100644
--- a/tests/test_metrics/test_hmean_iou.py
+++ b/tests/test_metrics/test_hmean_iou.py
@@ -68,9 +68,10 @@ class TestHmeanIOU(unittest.TestCase):
         pred_data_sample = TextDetDataSample()
         pred_data_sample.pred_instances = InstanceData()
         pred_data_sample.pred_instances.polygons = [
+            torch.FloatTensor([0, 0, 1, 0, 1, 1, 0, 1]),
             torch.FloatTensor([0, 0, 1, 0, 1, 1, 0, 1])
         ]
-        pred_data_sample.pred_instances.scores = torch.FloatTensor([0.8])
+        pred_data_sample.pred_instances.scores = torch.FloatTensor([1, 0.95])
         predictions.append(pred_data_sample.to_dict())
 
         self.predictions = predictions
@@ -81,7 +82,7 @@ class TestHmeanIOU(unittest.TestCase):
         metric.process(self.gt, self.predictions)
         eval_results = metric.evaluate(size=2)
 
-        precision = 3 / 4
+        precision = 3 / 5
         recall = 3 / 4
         hmean = 2 * precision * recall / (precision + recall)
         target_result = {