open-mmlab · ZwwWayne · Aug 30, 2022 · Aug 29, 2022 · Aug 30, 2022 · Aug 30, 2022
diff --git a/docs/en/1_exist_data_model.md b/docs/en/1_exist_data_model.md
diff --git a/docs/en/2_new_data_model.md b/docs/en/2_new_data_model.md
diff --git a/docs/en/conventions.md → docs/en/advanced_guides/conventions.md b/docs/en/conventions.md → docs/en/advanced_guides/conventions.md
diff --git a/docs/en/tutorials/customize_dataset.md → docs/en/advanced_guides/customize_dataset.md b/docs/en/tutorials/customize_dataset.md → docs/en/advanced_guides/customize_dataset.md
@@ -1,4 +1,4 @@
-# Tutorial 2: Customize Datasets
+# Customize Datasets
 
 ## Support new data format
 
@@ -183,7 +183,7 @@ We use this way to support CityScapes dataset. The script is in [cityscapes.py](
 ### Reorganize new data format to middle format
 
 It is also fine if you do not want to convert the annotation format to COCO or PASCAL format.
-Actually, we define a simple annotation format in MMEninge's [BaseDataset ](https://github.com/open-mmlab/mmengine/blob/main/mmengine/dataset/base_dataset.py#L116) and all existing datasets are
+Actually, we define a simple annotation format in MMEninge's [BaseDataset](https://github.com/open-mmlab/mmengine/blob/main/mmengine/dataset/base_dataset.py#L116) and all existing datasets are
 processed to be compatible with it, either online or offline.
 
 The annotation of the dataset must be in `json` or `yaml`, `yml` or `pickle`, `pkl` format; the dictionary stored in the annotation file must contain two fields `metainfo` and `data_list`.  The `metainfo` is a dictionary, which contains the metadata of the dataset, such as class information; `data_list` is a list, each element in the list is a dictionary, the dictionary defines the raw data of one image, and each raw data contains a or several training/testing samples.
@@ -237,7 +237,7 @@ Here is an example.
 
 Some datasets may provide annotations like crowd/difficult/ignored bboxes, we use `ignore_flag`to cover them.
 
-After obtaining the above standard data annotation format, you can directly use [BaseDetDataset](<>) of MMDetection in the configuration , without conversion.
+After obtaining the above standard data annotation format, you can directly use [BaseDetDataset](mmdet.datasets.BaseDetDataset) of MMDetection in the configuration , without conversion.
 
 ### An example of customized dataset
 

diff --git a/docs/en/tutorials/customize_losses.md → docs/en/advanced_guides/customize_losses.md b/docs/en/tutorials/customize_losses.md → docs/en/advanced_guides/customize_losses.md
@@ -1,4 +1,4 @@
-# Tutorial 6: Customize Losses
+# Customize Losses
 
 MMDetection provides users with different loss functions. But the default configuration may be not applicable for different datasets or models, so users may want to modify a specific loss to adapt the new situation.
 
@@ -40,7 +40,7 @@ For some other losses which have positive and negative sample balance mechanism
 ## Tweaking loss
 
 Tweaking a loss is more related with step 2, 4, 5, and most modifications can be specified in the config.
-Here we take [Focal Loss (FL)](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/focal_loss.py) as an example.
+Here we take [Focal Loss (FL)](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/losses/focal_loss.py) as an example.
 The following code sniper are the construction method and config of FL respectively, they are actually one to one correspondence.
 
 ```python

diff --git a/docs/en/tutorials/customize_models.md → docs/en/advanced_guides/customize_models.md b/docs/en/tutorials/customize_models.md → docs/en/advanced_guides/customize_models.md
@@ -1,4 +1,4 @@
-# Tutorial 4: Customize Models
+# Customize Models
 
 We basically categorize model components into 5 types.
 

diff --git a/docs/en/tutorials/customize_runtime.md → docs/en/advanced_guides/customize_runtime.md b/docs/en/tutorials/customize_runtime.md → docs/en/advanced_guides/customize_runtime.md
@@ -1,4 +1,4 @@
-# Tutorial 5: Customize Runtime Settings
+# Customize Runtime Settings
 
 ## Customize optimization settings
 
@@ -140,7 +140,7 @@ Tricks not implemented by the optimizer should be implemented through optimizer
       _delete_=True, clip_grad=dict(max_norm=35, norm_type=2))
   ```
 
-  If your config inherits the base config which already sets the `optim_wrapper`, you might need `_delete_=True` to override the unnecessary settings. See the [config documentation](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html) for more details.
+  If your config inherits the base config which already sets the `optim_wrapper`, you might need `_delete_=True` to override the unnecessary settings. See the [config documentation](../user_guides/config.md) for more details.
 
 - __Use momentum schedule to accelerate model convergence__:
   We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way.
@@ -253,7 +253,7 @@ train_cfg = dict(
 
 #### 1. Implement a new hook
 
-MMEngine provides many useful [hooks](https://mmdetection.readthedocs.io/en/latest/tutorials/hooks.html), but there are some occasions when the users might need to implement a new hook. MMDetection supports customized hooks in training in v3.0 . Thus the users could implement a hook directly in mmdet or their mmdet-based codebases and use the hook by only modifying the config in training.
+MMEngine provides many useful [hooks](https://mmengine.readthedocs.io/en/latest/tutorials/hooks.html), but there are some occasions when the users might need to implement a new hook. MMDetection supports customized hooks in training in v3.0 . Thus the users could implement a hook directly in mmdet or their mmdet-based codebases and use the hook by only modifying the config in training.
 Here we give an example of creating a new hook in mmdet and using it in training.
 
 ```python