diff --git a/README.md b/README.md
index 1c7bffc8..434a1255 100644
--- a/README.md
+++ b/README.md
@@ -47,6 +47,10 @@ See [INSTALLATION](docs/en/installation.md) for details.
 
 See [GETTING STARTED](GETTING_STARTED.md) for details.
 
+## Custom dataset examples
+
+See [examples](examples)
+
 ## Notes
 
 ⚠️ The current version is based on the [static shape of GRAPH](https://mindspore.cn/docs/en/r2.0/note/static_graph_syntax_support.html). 
diff --git a/docs/zh/how_to_guides/data_preparation.md b/docs/zh/how_to_guides/data_preparation.md
index c5c0d076..975ecd10 100644
--- a/docs/zh/how_to_guides/data_preparation.md
+++ b/docs/zh/how_to_guides/data_preparation.md
@@ -6,13 +6,13 @@
 ```
 └─ coco2017_yolo
     ├─ annotations
-        └─ instances_val2017.json
+    │    └─ instances_val2017.json
     ├─ images
-        ├─ train2017   # coco2017 原始图片
-        └─ val2017     # coco2017 原始图片
+    │    ├─ train2017   # coco2017 原始图片
+    │    └─ val2017     # coco2017 原始图片
     ├─ labels
-        ├─ train2017
-        └─ val2017
+    │    ├─ train2017
+    │    └─ val2017
     ├─ train2017.txt
     ├─ val2017.txt
     └─ test-dev2017.txt
diff --git a/examples/finetune_car_detection/crejson.py b/examples/finetune_car_detection/crejson.py
index 6eda17a4..1bb24f65 100644
--- a/examples/finetune_car_detection/crejson.py
+++ b/examples/finetune_car_detection/crejson.py
@@ -82,4 +82,3 @@ def init_coco_format():
     # 为每个分区保存JSON文件
 with open(f"./annotations/instances_val2017.json", "w") as json_file:
     json.dump(coco_format, json_file, indent=4)
-  
\ No newline at end of file
diff --git a/examples/finetune_car_detection/rename.py b/examples/finetune_car_detection/rename.py
index 11a2f7c3..6ef420a2 100644
--- a/examples/finetune_car_detection/rename.py
+++ b/examples/finetune_car_detection/rename.py
@@ -5,7 +5,7 @@
 start_num = 1
 
 # 获取当前目录下所有的.txt文件
-files = [f for f in os.listdir('images/train') if f.endswith(extension)]
+files = [f for f in os.listdir('images/val') if f.endswith(extension)]
 
 for i, file in enumerate(files, start=start_num):
     # 构造新的文件名，使用zfill填充前导零
@@ -13,10 +13,9 @@
 
     # 使用os.rename重命名文件
     # 需根据自己的路径修改
-    os.chdir('D:/Desktop/mindspore/yolo_car_detection/bddua/bdd100k/images/train')
+    os.chdir('images/val')
     os.rename(file, new_filename)
 
     #print(f"Renamed '{file}' to '{new_filename}'")
 
 print("All files have been renamed.")
-  
diff --git a/examples/finetune_car_detection/voc2yolo.py b/examples/finetune_car_detection/voc2yolo.py
index 0b514eec..9903645f 100644
--- a/examples/finetune_car_detection/voc2yolo.py
+++ b/examples/finetune_car_detection/voc2yolo.py
@@ -1,7 +1,6 @@
 import xml.etree.ElementTree as ET
 import sys
 import os.path
-import cv2
 
 
 class XmlParse:
@@ -43,7 +42,7 @@ def xml2txt(xml, labels, name_list, img_path):
         fil_name = root.find('filename').text[:-4]
         if not os.path.exists(labels):  # 如果路径不存在则创建
             os.mkdir(labels)
-        out = open(labels + './' + fil_name + '.txt', 'w+')
+        out = open(labels + '/' + fil_name + '.txt', 'w+')
         for obj in root.iter('object'):
 
             x_min = float(obj.find('bndbox').find('xmin').text)
@@ -74,8 +73,7 @@ def xml2txt(xml, labels, name_list, img_path):
             # print(w)
             # print(h)
 
-            #class_dict = dict(zip(name_list, range(0, len(name_list))))
-            class_dict={'rider':0, 'pedestrian':1, 'trailer':2, 'train':3,'bus':4,'car':5,'truck':6,'traffic sign':7,'traffic light':8,'other person':9,'motorcycle':10,'bicycle':11}
+            class_dict = {name: i for i, name in enumerate(name_list)}
             class_name = obj.find('name').text
             if class_name not in name_list:
                 pass
@@ -100,8 +98,7 @@ def folder_Path():
     img_path = './images/val'
     xml_path = './labels_xml/val'  # xml路径
     labels = './val'  # 转txt路径
-    #name_list = ['rider', 'pedestrian', 'trailer', 'train','bus','car','truck','traffic sign','traffic light','other person','motorcycle','bicycle']  # 类别名
-    name_list = ['rider', 'pedestrian', 'trailer', 'train','bus','car','truck','traffic sign','traffic light','other person','motorcycle','bicycle',"van"]  # 类别名
+    name_list = ['rider', 'pedestrian', 'trailer', 'train', 'bus', 'car', 'truck', 'traffic sign', 'traffic light', 'other person', 'motorcycle', 'bicycle', "van"]  # 类别名
 
     xml2txt(xml_path, labels, name_list, img_path)
 
diff --git a/examples/finetune_single_class_dataset/README.md b/examples/finetune_single_class_dataset/README.md
new file mode 100644
index 00000000..bb4d18fe
--- /dev/null
+++ b/examples/finetune_single_class_dataset/README.md
@@ -0,0 +1,63 @@
+### 单类别数据集训练流程
+
+本文以自制巧克力花生豆数据集为例，介绍单类别数据集使用MindYOLO进行训练的主要流程。
+
+#### 数据集格式转换
+
+巧克力花生豆数据集采用voc格式的数据标注，其文件目录如下所示：
+```
+             ROOT_DIR
+                ├── Annotations
+                │        ├── 000000.xml
+                │        └── 000002.xml
+                ├── Images
+                │       ├── 000000.jpg
+                │       └── 000002.jpg
+                └── Test_Images
+                      ├── 000004.jpg
+                      └── 000006.jpg
+```
+数据集格式转换分为以下步骤：
+
+1. 训练集与验证集转换为yolo格式。可参考[voc2yolo.py](../finetune_car_detection/voc2yolo.py)，使用时需修改图片文件夹路径、标签文件夹路径与生成的txt标签文件夹路径，且对训练集和验证集依次完成该过程。
+2. 验证集转换为coco格式。首先完成图片重命名，可参考[rename.py
+](../finetune_car_detection/rename.py)，使用时需确保当前目录为数据集的根目录。然后生成json文件，可参考[crejson.py](../finetune_car_detection/crejson.py)，使用时需修改验证集图片文件夹路径，验证集txt标注文件路径以及生成的json文件路径。
+
+#### 编写yaml配置文件
+配置文件继承[yolov8n.yaml](../../configs/yolov8/yolov8n.yaml)，并且列出需要修改的参数，通常包括数据集相关参数以及学习率等超参，如下所示：
+```
+__BASE__: [
+  '../../configs/yolov8/yolov8n.yaml',
+]
+
+data:
+  dataset_name: seed
+  train_set: ./seed/train.txt
+  val_set: ./seed/val.txt
+  nc: 1
+  # class names
+  names: [ 'seed' ]
+
+optimizer:
+  lr_init: 0.001  # initial learning rate
+  warmup_bias_lr: 0.01 # warmup initial bias lr
+  min_warmup_step: 10 # minmum warmup step
+```
+#### 模型训练
+选用yolov8n模型进行训练。
+* 在多卡NPU/GPU上进行分布式模型训练，以8卡为例:
+
+  ```shell
+  mpirun --allow-run-as-root -n 8 python train.py --config ./examples/finetune_single_class_dataset/yolov8n_single_class_dataset.yaml --is_parallel True
+  ```
+
+#### 可视化推理
+使用/demo/predict.py即可用训练好的模型进行可视化推理，运行方式如下：
+
+```shell
+python demo/predict.py --config ./examples/finetune_single_class_dataset/yolov8n_single_class_dataset.yaml --weight=/path_to_ckpt/WEIGHT.ckpt --image_path /path_to_image/IMAGE.jpg
+```
+推理效果如下：
+<div align=center>
+<img width='600' src="https://github.com/yuedongli1/images/raw/master/16.jpg"/>
+</div>
\ No newline at end of file
diff --git a/examples/finetune_single_class_dataset/yolov8n_single_class_dataset.yaml b/examples/finetune_single_class_dataset/yolov8n_single_class_dataset.yaml
new file mode 100644
index 00000000..80bd0768
--- /dev/null
+++ b/examples/finetune_single_class_dataset/yolov8n_single_class_dataset.yaml
@@ -0,0 +1,16 @@
+__BASE__: [
+  '../../configs/yolov8/yolov8n.yaml',
+]
+
+data:
+  dataset_name: seed
+  train_set: ./seed/train.txt
+  val_set: ./seed/val.txt
+  nc: 1
+  # class names
+  names: [ 'seed' ]
+
+optimizer:
+  lr_init: 0.001  # initial learning rate
+  warmup_bias_lr: 0.01 # warmup initial bias lr
+  min_warmup_step: 10 # minmum warmup step
diff --git a/requirements/cpu_requirements.txt b/requirements/cpu_requirements.txt
index 2fc8b85a..aeacfbd1 100644
--- a/requirements/cpu_requirements.txt
+++ b/requirements/cpu_requirements.txt
@@ -2,7 +2,7 @@ numpy < 2
 pyyaml >= 5.3
 tqdm
 pillow == 9.5.0
-mindspore
+mindspore <= 2.3.0
 pylint
 pytest
 opencv-python