diff --git a/tutorials/custom_dataset.md b/tutorials/custom_dataset.md
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-# 数据集格式介绍
-
-适用于MindYOLO的数据集格式具有如下形式：
-
-下载coco2017 YOLO格式 [coco2017labels-segments](https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip) 以及coco2017 原始图片 [train2017](http://images.cocodataset.org/zips/train2017.zip) , [val2017](http://images.cocodataset.org/zips/val2017.zip) ，然后将coco2017 原始图片放到coco2017 YOLO格式 images目录下：
-```
-└─ coco2017_yolo
-    ├─ annotations
-        └─ instances_val2017.json
-    ├─ images
-        ├─ train2017   # coco2017 原始图片
-        └─ val2017     # coco2017 原始图片
-    ├─ labels
-        ├─ train2017
-        └─ val2017
-    ├─ train2017.txt
-    ├─ val2017.txt
-    └─ test-dev2017.txt
-```
-其中train.txt文件每行对应单张图片的相对路径，例如：
-```
-./images/train/00000000.jpg
-./images/train/00000001.jpg
-./images/train/00000002.jpg
-./images/train/00000003.jpg
-./images/train/00000004.jpg
-./images/train/00000005.jpg
-```
-labels下的train2017文件夹下的txt文件为相应图片的标注信息，支持detect和segment两种格式。
-
-detect格式：通常每行有5列，分别对应类别id以及标注框归一化之后的中心点坐标xy和宽高wh
-```
-62 0.417040 0.206280 0.403600 0.412560
-62 0.818810 0.197933 0.174740 0.189680
-39 0.684540 0.277773 0.086240 0.358960
-0 0.620220 0.725853 0.751680 0.525840
-63 0.197190 0.364053 0.394380 0.669653
-39 0.932330 0.226240 0.034820 0.076640
-```
-
-segment格式：每行第一个数据为类别id，后续为两两成对的归一化坐标点x,y
-
-```
-45 0.782016 0.986521 0.937078 0.874167 0.957297 0.782021 0.950562 0.739333 0.825844 0.561792 0.714609 0.420229 0.657297 0.391021 0.608422 0.4 0.0303438 0.750562 0.0016875 0.811229 0.003375 0.889896 0.0320156 0.986521
-45 0.557859 0.143813 0.487078 0.0314583 0.859547 0.00897917 0.985953 0.130333 0.984266 0.184271 0.930344 0.386521 0.80225 0.480896 0.763484 0.485396 0.684266 0.39775 0.670781 0.3955 0.679219 0.310104 0.642141 0.253937 0.561234 0.155063 0.559547 0.137083
-50 0.39 0.727063 0.418234 0.649417 0.455297 0.614125 0.476469 0.614125 0.51 0.590583 0.54 0.569417 0.575297 0.562354 0.601766 0.56 0.607062 0.536479 0.614125 0.522354 0.637063 0.501167 0.665297 0.48 0.69 0.477646 0.698828 0.494125 0.698828 0.534125 0.712938 0.529417 0.742938 0.548229 0.760594 0.564708 0.774703 0.550583 0.778234 0.536479 0.781766 0.531771 0.792359 0.541167 0.802937 0.555292 0.802937 0.569417 0.802937 0.576479 0.822359 0.576479 0.822359 0.597646 0.811766 0.607062 0.811766 0.618833 0.818828 0.637646 0.820594 0.656479 0.827641 0.687063 0.827641 0.703521 0.829406 0.727063 0.838234 0.708229 0.852359 0.729417 0.868234 0.750583 0.871766 0.792938 0.877063 0.821167 0.884125 0.861167 0.817062 0.92 0.734125 0.976479 0.711172 0.988229 0.48 0.988229 0.494125 0.967063 0.517062 0.912937 0.508234 0.832937 0.485297 0.788229 0.471172 0.774125 0.395297 0.729417
-45 0.375219 0.0678333 0.375219 0.0590833 0.386828 0.0503542 0.424156 0.0315208 0.440797 0.0281458 0.464 0.0389167 0.525531 0.115583 0.611797 0.222521 0.676359 0.306583 0.678875 0.317354 0.677359 0.385271 0.66475 0.394687 0.588594 0.407458 0.417094 0.517771 0.280906 0.604521 0.0806562 0.722208 0.0256719 0.763917 0.00296875 0.809646 0 0.786104 0 0.745083 0 0.612583 0.03525 0.613271 0.0877187 0.626708 0.130594 0.626708 0.170437 0.6025 0.273844 0.548708 0.338906 0.507 0.509906 0.4115 0.604734 0.359042 0.596156 0.338188 0.595141 0.306583 0.595141 0.291792 0.579516 0.213104 0.516969 0.129042 0.498297 0.100792 0.466516 0.0987708 0.448875 0.0786042 0.405484 0.0705208 0.375219 0.0678333 0.28675 0.108375 0.282719 0.123167 0.267078 0.162854 0.266062 0.189083 0.245391 0.199833 0.203516 0.251625 0.187375 0.269771 0.159641 0.240188 0.101125 0.249604 0 0.287271 0 0.250271 0 0.245563 0.0975938 0.202521 0.203516 0.145354 0.251953 0.123167 0.28675 0.108375
-49 0.587812 0.128229 0.612281 0.0965625 0.663391 0.0840833 0.690031 0.0908125 0.700109 0.10425 0.705859 0.133042 0.700109 0.143604 0.686422 0.146479 0.664828 0.153188 0.644672 0.157042 0.629563 0.175271 0.605797 0.181021 0.595 0.147437
-49 0.7405 0.178417 0.733719 0.173896 0.727781 0.162583 0.729484 0.150167 0.738812 0.124146 0.747281 0.0981458 0.776109 0.0811875 0.804094 0.0845833 0.814266 0.102667 0.818516 0.115104 0.812578 0.133208 0.782906 0.151292 0.754063 0.172771
-49 0.602656 0.178854 0.636125 0.167875 0.655172 0.165125 0.6665 0.162375 0.680391 0.155521 0.691719 0.153458 0.703047 0.154146 0.713859 0.162375 0.724156 0.174729 0.730844 0.193271 0.733422 0.217979 0.733938 0.244063 0.733422 0.281813 0.732391 0.295542 0.728266 0.300354 0.702016 0.294854 0.682969 0.28525 0.672156 0.270146
-49 0.716891 0.0519583 0.683766 0.0103958 0.611688 0.0051875 0.568828 0.116875 0.590266 0.15325 0.590266 0.116875 0.613641 0.0857083 0.631172 0.0857083 0.6565 0.083125 0.679875 0.0883125 0.691563 0.0961042 0.711031 0.0649375
-
-```
-
-instances_val.json为coco格式的验证集标注，可直接调用coco api用于map的计算。
-
-训练&推理时，需修改`configs/coco.yaml`中的`train_set`,`val_set`,`test_set`为真实数据路径
-
-使用MindYOLO套件完成自定义数据集finetune的实际案例可参考[README.md](https://github.com/mindspore-lab/mindyolo/blob/master/examples/finetune_SHWD/README.md)
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