diff --git a/docs/zh_cn/tutorials/evaluator.md b/docs/zh_cn/tutorials/evaluator.md
index d2ab35b66168753846a9e122f3de9b431b546259..00649d582ff2ad95727acdc1b777ec989fd606bc 100644
--- a/docs/zh_cn/tutorials/evaluator.md
+++ b/docs/zh_cn/tutorials/evaluator.md
@@ -37,8 +37,8 @@ validation_cfg=dict(
 ```python
 validation_cfg=dict(
     evaluator=[
-        dict(type='accuracy', top_k=1),  # 使用分类正确率评测器
-        dict(type='f1_score')  # 使用 F1_score 评测器
+        dict(type='Accuracy', top_k=1),  # 使用分类正确率评测器
+        dict(type='F1Score')  # 使用 F1_score 评测器
     ],
     main_metric='accuracy'
     interval=10,
@@ -51,8 +51,8 @@ validation_cfg=dict(
 ```python
 validation_cfg=dict(
     evaluator=[
-        dict(type='accuracy', top_k=1, prefix='top1'),
-        dict(type='accuracy', top_k=5, prefix='top5')
+        dict(type='Accuracy', top_k=1, prefix='top1'),
+        dict(type='Accuracy', top_k=5, prefix='top5')
     ],
     main_metric='top1_accuracy',  # 前缀 'top1' 被自动添加进指标名称中，用以区分同名指标
     interval=10,
@@ -62,7 +62,7 @@ validation_cfg=dict(
 
 ## 增加自定义评测器
 
-在 OpenMMLab 的各个算法库中，已经实现了对应方向的常用评测器。如 MMDetection 中提供了 COCO 评测器，MMClassification 中提供了 accuracy、f1_score 等评测器等。
+在 OpenMMLab 的各个算法库中，已经实现了对应方向的常用评测器。如 MMDetection 中提供了 COCO 评测器，MMClassification 中提供了 Accuracy、F1Score 等评测器等。
 
 用户也可以根据自身需求，增加自定义的评测器。在实现自定义评测器时，用户需要继承 MMEngine 中提供的评测器基类 [BaseEvaluator](Todo:baseevaluator-doc-link)，并实现对应的抽象方法。
 
@@ -96,8 +96,8 @@ from mmengine.registry import EVALUATORS
 import numpy as np
 
 @EVALUATORS.register_module()
-class AccuracyEvaluator(BaseEvaluator):
-
+class Accuracy(BaseEvaluator):
+   
     def process(self, data_samples: Dict, predictions: Dict):
         """Process one batch of data and predictions. The processed
         Results should be stored in `self.results`, which will be used
diff --git a/tests/test_evaluator/test_base_evaluator.py b/tests/test_evaluator/test_base_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..382c8b4ebe352d8ef98ede630cd690a7b68f89f6
--- /dev/null
+++ b/tests/test_evaluator/test_base_evaluator.py
@@ -0,0 +1,136 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+from typing import Dict, List, Optional
+from unittest import TestCase
+
+import numpy as np
+
+from mmengine.evaluator import BaseEvaluator, ComposedEvaluator
+from mmengine.registry import EVALUATORS
+
+
+@EVALUATORS.register_module()
+class ToyEvaluator(BaseEvaluator):
+
+    def __init__(self,
+                 collect_device: str = 'cpu',
+                 dummy_metrics: Optional[Dict] = None):
+        super().__init__(collect_device=collect_device)
+        self.dummy_metrics = dummy_metrics
+
+    def process(self, data_samples, predictions):
+        result = {'pred': predictions['pred'], 'label': data_samples['label']}
+        self.results.append(result)
+
+    def compute_metrics(self, results: List):
+        if self.dummy_metrics is not None:
+            assert isinstance(self.dummy_metrics, dict)
+            return self.dummy_metrics.copy()
+
+        pred = np.concatenate([result['pred'] for result in results])
+        label = np.concatenate([result['label'] for result in results])
+        acc = (pred == label).sum() / pred.size
+
+        metrics = {
+            'accuracy': acc,
+            'size': pred.size,  # To check the number of testing samples
+        }
+
+        return metrics
+
+
+def generate_test_results(size, batch_size, pred, label):
+    num_batch = math.ceil(size / batch_size)
+    bs_residual = size % batch_size
+    for i in range(num_batch):
+        bs = bs_residual if i == num_batch - 1 else batch_size
+        data_samples = {'label': np.full(bs, label)}
+        predictions = {'pred': np.full(bs, pred)}
+        yield (data_samples, predictions)
+
+
+class TestBaseEvaluator(TestCase):
+
+    def build_evaluator(self, cfg):
+        if isinstance(cfg, (list, tuple)):
+            evaluators = [EVALUATORS.build(_cfg) for _cfg in cfg]
+            return ComposedEvaluator(evaluators=evaluators)
+        else:
+            return EVALUATORS.build(cfg)
+
+    def test_single_evaluator(self):
+        cfg = dict(type='ToyEvaluator')
+        evaluator = self.build_evaluator(cfg)
+
+        size = 10
+        batch_size = 4
+
+        for data_samples, predictions in generate_test_results(
+                size, batch_size, pred=1, label=1):
+            evaluator.process(data_samples, predictions)
+
+        metrics = evaluator.evaluate(size=size)
+        self.assertAlmostEqual(metrics['accuracy'], 1.0)
+        self.assertEqual(metrics['size'], size)
+
+        # Test empty results
+        cfg = dict(type='ToyEvaluator', dummy_metrics=dict(accuracy=1.0))
+        evaluator = self.build_evaluator(cfg)
+        with self.assertWarnsRegex(UserWarning, 'got empty `self._results`.'):
+            evaluator.evaluate(0)
+
+    def test_composed_evaluator(self):
+        cfg = [
+            dict(type='ToyEvaluator'),
+            dict(type='ToyEvaluator', dummy_metrics=dict(mAP=0.0))
+        ]
+
+        evaluator = self.build_evaluator(cfg)
+
+        size = 10
+        batch_size = 4
+
+        for data_samples, predictions in generate_test_results(
+                size, batch_size, pred=1, label=1):
+            evaluator.process(data_samples, predictions)
+
+        metrics = evaluator.evaluate(size=size)
+
+        self.assertAlmostEqual(metrics['accuracy'], 1.0)
+        self.assertAlmostEqual(metrics['mAP'], 0.0)
+        self.assertEqual(metrics['size'], size)
+
+    def test_ambiguate_metric(self):
+
+        cfg = [
+            dict(type='ToyEvaluator', dummy_metrics=dict(mAP=0.0)),
+            dict(type='ToyEvaluator', dummy_metrics=dict(mAP=0.0))
+        ]
+
+        evaluator = self.build_evaluator(cfg)
+
+        size = 10
+        batch_size = 4
+
+        for data_samples, predictions in generate_test_results(
+                size, batch_size, pred=1, label=1):
+            evaluator.process(data_samples, predictions)
+
+        with self.assertRaisesRegex(
+                ValueError,
+                'There are multiple evaluators with the same metric name'):
+            _ = evaluator.evaluate(size=size)
+
+    def test_dataset_meta(self):
+        dataset_meta = dict(classes=('cat', 'dog'))
+
+        cfg = [
+            dict(type='ToyEvaluator'),
+            dict(type='ToyEvaluator', dummy_metrics=dict(mAP=0.0))
+        ]
+
+        evaluator = self.build_evaluator(cfg)
+        evaluator.dataset_meta = dataset_meta
+
+        for _evaluator in evaluator.evaluators:
+            self.assertDictEqual(_evaluator.dataset_meta, dataset_meta)