revise eng docs

README.md

@@ -124,7 +124,7 @@ For detailed user guides and advanced guides, please refer to our [documentation
 
   </details>
 
-We also provide [colab tutorial](demo/MMDet_Tutorial.ipynb) and [instance segmentation colab tutorial](demo/MMDet_InstanceSeg_Tutorial.ipynb).
+We also provide object detection colab tutorial [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb) and instance segmentation colab tutorial [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_InstanceSeg_Tutorial.ipynb).
 
 To migrate from MMDetection 2.x, please refer to [migration](https://mmdetection.readthedocs.io/en/3.x/migration.html).
 

README_zh-CN.md

@@ -122,7 +122,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
   </details>
 
-我们提供了 [检测的 colab 教程](demo/MMDet_Tutorial.ipynb) 和 [实例分割的 colab 教程](demo/MMDet_InstanceSeg_Tutorial.ipynb)
+我们提供了检测的 colab 教程 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb) 和 实例分割的 colab 教程 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb)
 
 同时，我们还提供了 [MMDetection 中文解读文案汇总](docs/zh_cn/article.md)
 

docs/en/get_started.md

@@ -86,7 +86,7 @@ The downloading will take several seconds or more, depending on your network env
 Option (a). If you install MMDetection from source, just run the following command.
 
 ```shell
-python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg
+python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_8xb24-320-300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg
 ```
 
 You will see a new image `result.jpg` on your current folder, where bounding boxes are plotted on cars, benches, etc.
@@ -98,7 +98,7 @@ from mmdet.apis import init_detector, inference_detector
 from mmdet.utils import register_all_modules
 
 register_all_modules()
-config_file = 'yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py'
+config_file = 'yolov3_mobilenetv2_8xb24-320-300e_coco.py'
 checkpoint_file = 'yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth'
 model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
 inference_detector(model, 'demo/demo.jpg')
@@ -205,7 +205,7 @@ print(mmdet.__version__)
 Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python.
 ```
 
-#### Using MMDetection with Docker
+#### Use MMDetection with Docker
 
 We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03.
 
@@ -225,3 +225,13 @@ docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetect
 
 If you have some issues during the installation, please first view the [FAQ](notes/faq.md) page.
 You may [open an issue](https://github.com/open-mmlab/mmdetection/issues/new/choose) on GitHub if no solution is found.
+
+### Use Multiple Versions of MMDetection in Develoement
+
+Training and testing scripts have already been modified in `PYTHONPATH` in order to make sure the scripts are using their own versions of MMDetection.
+
+To install the default version of MMDetection in your environment, you can exclude the follow code in the relative scripts:
+
+```shell
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
+```

docs/en/user_guides/config.md

@@ -325,9 +325,9 @@ param_scheduler = [
 
 ### Hook config
 
-Users can attach hooks to training, validation, and testing loops to insert some oprations during running. There are two different hook fields, one is `default_hooks` and the other is `custom_hooks`.
+Users can attach Hooks to training, validation, and testing loops to insert some oprations during running. There are two different hook fields, one is `default_hooks` and the other is `custom_hooks`.
 
-`default_hooks` is a dict of hook configs. `default_hooks` are the hooks must required at runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to `None`.
+`default_hooks` is a dict of hook configs, and they are the hooks must be required at the runtime. They have default priority which should not be modified. If not set, runner will use the default values. To disable a default hook, users can set its config to `None`.
 
 ```python
 default_hooks = dict(
@@ -339,7 +339,7 @@ default_hooks = dict(
     visualization=dict(type='DetVisualizationHook'))
 ```
 
-`custom_hooks` is a list of hook configs. Users can develop their own hooks and insert them in this field.
+`custom_hooks` is a list of all other hook configs. Users can develop their own hooks and insert them in this field.
 
 ```python
 custom_hooks = []
@@ -412,8 +412,8 @@ log_processor = dict(by_epoch=False)
 ## Config file inheritance
 
 There are 4 basic component types under `config/_base_`, dataset, model, schedule, default_runtime.
-Many methods could be easily constructed with one of each like Faster R-CNN, Mask R-CNN, Cascade R-CNN, RPN, SSD.
-The configs that are composed by components from `_base_` are called _primitive_.
+Many methods could be easily constructed with one of these models like Faster R-CNN, Mask R-CNN, Cascade R-CNN, RPN, SSD.
+The configs that are composed by components from `_base_` are called the _primitive_.
 
 For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3.
 
@@ -426,13 +426,13 @@ Please refer to [mmengine config tutorial](https://mmengine.readthedocs.io/en/la
 
 By setting the `_base_` field, we can set which files the current configuration file inherits from.
 
-When `_base_` is a string of a file path, it means inherit the contents of one config file.
+When `_base_` is a string of a file path, it means inheriting the contents from one config file.
 
 ```python
 _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
 ```
 
-When `_base_` is a list of multiple file paths, it means inheriting multiple files.
+When `_base_` is a list of multiple file paths, it means inheriting from multiple files.
 
 ```python
 _base_ = [
@@ -581,7 +581,7 @@ When submitting jobs using `tools/train.py` or `tools/test.py`, you may specify
 
   If the value to be updated is a list or a tuple. For example, the config file normally sets `model.data_preprocessor.mean=[123.675, 116.28, 103.53]`. If you want to
   change the mean values, you may specify `--cfg-options model.data_preprocessor.mean="[127,127,127]"`. Note that the quotation mark `"` is necessary to
-  support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value.
+  support list/tuple data types, and **NO** white space is allowed inside the quotation marks in the specified value.
 
 ## Config name style
 

docs/en/user_guides/dataset_prepare.md

@@ -34,7 +34,7 @@ mmdetection
 │   │   ├── VOC2012
 ```
 
-Some models require additional [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) datasets, such as HTC, DetectoRS and SCNet, you can download and unzip then move to the coco folder. The directory should be like this.
+Some models require additional [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) datasets, such as HTC, DetectoRS and SCNet, you can download, unzip, and then move them to the coco folder. The directory should be like this.
 
 ```text
 mmdetection
@@ -47,7 +47,7 @@ mmdetection
 │   │   ├── stuffthingmaps
 ```
 
-Panoptic segmentation models like PanopticFPN require additional [COCO Panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) datasets, you can download and unzip then move to the coco annotation folder. The directory should be like this.
+Panoptic segmentation models like PanopticFPN require additional [COCO Panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) datasets, you can download, unzip, and then move them to the coco annotation folder. The directory should be like this.
 
 ```text
 mmdetection

docs/zh_cn/get_started.md

@@ -83,7 +83,7 @@ mim download mmdet --config yolov3_mobilenetv2_8xb24-320-300e_coco --dest .
 方案 1. 如果你通过源码安装的 MMDetection，那么直接运行以下命令进行验证：
 
 ```shell
-python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg
+python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_8xb24-320-300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg
 ```
 
 你会在当前文件夹中看到一个新的图像`result.jpg`，图像中包含有网络预测的检测框。
@@ -95,7 +95,7 @@ from mmdet.apis import init_detector, inference_detector
 from mmdet.utils import register_all_modules
 
 register_all_modules()
-config_file = 'yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py'
+config_file = 'yolov3_mobilenetv2_8xb24-320-300e_coco.py'
 checkpoint_file = 'yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth'
 model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
 inference_detector(model, 'demo/demo.jpg')
@@ -202,7 +202,7 @@ print(mmdet.__version__)
 ```
 
 ```{note}
-在 Jupyter 中，感叹号 `!` 用于执行外部命令，而 `%cd` 是一个[魔术命令](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd)，用于切换 Python 的工作路径。
+在 Jupyter Notebook 中，感叹号 `!` 用于执行外部命令，而 `%cd` 是一个[魔术命令](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd)，用于切换 Python 的工作路径。
 ```
 
 #### 通过 Docker 使用 MMDetection