Update README.md

README.md

@@ -33,8 +33,8 @@ pip install "mmdet>=3.0.0rc4"
 python app.py
 ```
 ##### The UI :
-1.Users can upload an image or use a sample image at ①.Then, they can select one of four models at ②. We recommend **Mask2Former**. After that, click *Run*.
-2.We provide two forms of segmentation results for download at ③.
+1. Users can upload an image or use a sample image at ①.Then, they can select one of four models at ②. We recommend **Mask2Former**. After that, click *Run*.
+2. We provide two forms of segmentation results for download at ③.
 ![alt text](img/app.png)
 
 ***Train and Test***
@@ -62,7 +62,7 @@ Here is an example if you want to make own dataset.
    ```bash
    python run/split_dataset.py
    ```
-     now, the structure looks like:
+    now, the structure looks like:
 <pre>
    📁 CVRPDataset/
    ├─📁 img_dir/
@@ -100,7 +100,7 @@ If you want to register your own dataset,
 ② Register dataset class in `mmseg/datasets/CVRP.py'
    ```python
    class CVRPDataset(BaseSegDataset):
-    METAINFO = {
+   METAINFO = {
         'classes':['background','panicle'],
         'palette':[[127,127,127],[200,0,0]]
     }
@@ -108,11 +108,11 @@ If you want to register your own dataset,
    
 ③ Modify pipeline of data process in `config/_base_/CVRP_pipeline.py`
    ```python
-   dataset_type = 'CVRPDataset' 
+    dataset_type = 'CVRPDataset' 
     data_root = 'CVRPDataset/'
    ```
-   you'll need to specify the paths for the train and evalution data directories.
-    ```python
+  you'll need to specify the paths for the train and evalution data directories.
+  ```python
     # train_dataloader: 
     data_prefix=dict(img_path='img_dir/train', seg_map_path='ann_dir/train')
     # val_dataloader: