Merge pull request #2 from linyq17/lyq-docs

[Docs] unify title format
open-mmlab · Nov 23, 2021 · cd56b00 · cd56b00
2 parents 29284a8 + c6daca4
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diff --git a/demo/README.md b/demo/README.md
@@ -1,8 +1,8 @@
 # Few Shot Classification Demo
 
-## 1 shot classification Demo (Metric Based Model)
+## One Shot Classification Demo (Metric Based Model)
 
-We provide a demo script to test a single query image, given directory of 1shot support images.
+We provide a demo script to test a single query image given the directory of one shot support images.
 The file names of support images will be used as class names.
 
 ```shell
@@ -35,7 +35,7 @@ python demo/demo_metric_classifier_1shot_inference.py \
 
 # Few Shot Detection Demo
 
-## Attention RPN inference with support instances Demo
+## Attention RPN Inference with Support Instances Demo
 
 We provide a demo script to test a single query image, given directory of support instance images.
 The file names of support images will be used as class names.

diff --git a/docs/classification/customize_config.md b/docs/classification/customize_config.md
@@ -27,7 +27,7 @@ When submitting jobs using "tools/classification/train.py" or "tools/classificat
   change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. 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.
 
-## Config Name Style
+## Config name style
 
 We follow the below style to name config files. Contributors are advised to follow the same style.
 
@@ -50,7 +50,7 @@ We follow the below style to name config files. Contributors are advised to foll
 
 
 
-## An Example of Baseline
+## An example of Baseline
 
 To help the users have a basic idea of a complete config and the modules in a modern classification system,
 we make brief comments on the config of Baseline for MiniImageNet in 5 way 1 shot setting as the following.

diff --git a/docs/classification/customize_dataset.md b/docs/classification/customize_dataset.md
@@ -205,7 +205,7 @@ class MyClassifier(BaseFewShotClassifier):
             raise ValueError()
 ```
 
-#### using customize dataset wrapper in config
+#### Using customize dataset wrapper in config
 Then in the config, to use `MyDatasetWrapper` you can modify the config as the following,
 ```python
 dataset_A_train = dict(

diff --git a/docs/classification/customize_runtime.md b/docs/classification/customize_runtime.md
@@ -2,7 +2,7 @@
 
 ## Customize optimization settings
 
-### Customize optimizer supported by Pytorch
+### Customize an optimizer supported by Pytorch
 
 We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the `optimizer` field of config files.
 For example, if you want to use `ADAM` (note that the performance could drop a lot), the modification could be as the following.
@@ -347,7 +347,7 @@ evaluation = dict(interval=1, metric='bbox')
 ```
 
 
-## Customize Meta Testing
+## Customize meta testing
 
 We already support two ways to handle the support data, fine-tuning and straight forwarding.
 To customize the code for a meta test task, we need to add a new function `test_my_single_task`

diff --git a/docs/classification/overview.md b/docs/classification/overview.md
@@ -6,7 +6,7 @@ Therefore, the design of MMFewShot target at data sampling, meta test and models
 Additionally, the modules in [mmcls](https://github.com/open-mmlab/mmclassification) can be imported and reused in the code or config.
 
 
-## Design of Data Sampling
+## Design of data sampling
 In MMFewShot, we suggest customizing the data pipeline using a dataset wrapper and modify the arguments in forward
 function when returning the dict with customize keys.
 
@@ -29,7 +29,7 @@ class CustomizeDataset:
 More details can refer to [Tutorial 2: Adding New Dataset](https://mmfewshot.readthedocs.io/en/latest/classification/customize_dataset.html)
 
 
-## Design of Models API
+## Design of model APIs
 Each model in MMFewShot should implement following functions to support meta testing.
 More details can refer to [Tutorial 3: Customize Models](https://mmfewshot.readthedocs.io/en/latest/classification/customize_models.html)
 
@@ -68,7 +68,7 @@ class BaseFewShotClassifier(BaseModule):
 ```
 
 
-## Design of Meta Testing
+## Design of meta testing
 Meta testing performs prediction on random sampled tasks multiple times.
 Each task contains support and query data.
 More details can refer to `mmfewshot/classification/apis/test.py`.

diff --git a/docs/detection/customize_config.md b/docs/detection/customize_config.md
@@ -27,7 +27,7 @@ When submitting jobs using "tools/train.py" or "tools/test.py", you may specify
   change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. 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.
 
-## Config File Naming Convention
+## Config file naming convention
 
 We follow the below style to name config files. Contributors are advised to follow the same style.
 
@@ -48,7 +48,7 @@ We follow the below style to name config files. Contributors are advised to foll
 - `{dataset}`: dataset like `coco`, `voc-split1`, `voc-split2` and `voc-split3`.
 - `{data setting}`: like `base-training` or `1shot-fine-tuning`.
 
-## An Example of TFA
+## An example of TFA
 
 To help the users have a basic idea of a complete config and the modules in a modern classification system,
 we make brief comments on the config of TFA in coco 10 shot fine-tuning setting as the following.

diff --git a/docs/detection/customize_dataset.md b/docs/detection/customize_dataset.md
@@ -1,7 +1,7 @@
 # Tutorial 2: Adding New Dataset
 
 
-## Customize Dataset
+## Customize dataset
 
 ### Load annotations from file
 Different from the config in mmdet using `ann_file` to load a single dataset, we use `ann_cfg` to support the complex few shot setting.
@@ -153,7 +153,7 @@ dataset.prepare_train_img(self, idx, 'query')
 ```
 
 
-## Customize Dataset Wrapper
+## Customize a new dataset wrapper
 In few shot setting, the various sampling logic is implemented by
 dataset wrapper.
 An example of customizing query-support data sampling logic for training:
@@ -263,7 +263,7 @@ dataset_A_train = dict(
 ```
 
 
-## Customize Dataloader Wrapper
+## Customize a dataloader wrapper
 We also support to iterate two different dataset simultaneously by dataloader wrapper.
 
 An example of customizing dataloader wrapper for query and support dataset:

diff --git a/docs/install.md b/docs/install.md
@@ -75,40 +75,15 @@ Or you can still install MMFewShot manually:
 
     Optionally you can compile mmcv from source if you need to develop both mmcv and mmdet. Refer to the [guide](https://github.com/open-mmlab/mmcv#installation) for details.
 
-2. Install MMClassification.
+2. Install MMClassification and MMDetection.
 
-    You can simply install mmclassification with the following command:
+    You can simply install mmclassification and mmdetection with the following command:
 
     ```shell
-    pip install mmcls
+    pip install mmcls mmdet
     ```
 
-    or clone the repository and then install it:
-
-    ```shell
-    git clone https://github.com/open-mmlab/mmclassification.git
-    cd mmclassification
-    pip install -r requirements/build.txt
-    python setup.py install
-    ```
-
-3. Install MMDetection.
-
-    You can simply install mmdetection with the following command:
-
-    ```shell
-    pip install mmdet
-    ```
-
-    or clone the repository and then install it:
-
-    ```shell
-    git clone https://github.com/open-mmlab/mmdetection.git
-    cd mmdetection
-    pip install -r requirements/build.txt
-    python setup.py install
-    ```
-4. Install MMFewShot.
+3. Install MMFewShot.
 
     You can simply install mmfewshot with the following command:
 
@@ -164,19 +139,8 @@ conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10
 # install the latest mmcv
 pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html
 
-# install mmcls
-git clone https://github.com/open-mmlab/mmclassification.git
-cd mmclassification
-pip install -r requirements/build.txt
-python setup.py install
-cd ..
-
-# install mmdetection
-git clone https://github.com/open-mmlab/mmdetection.git
-cd mmdetection
-pip install -r requirements/build.txt
-python setup.py install
-cd ..
+# install mmclassification mmdetection
+pip install mmcls mmdet
 
 # install mmfewshot
 git clone https://github.com/open-mmlab/mmfewshot.git

diff --git a/docs_zh-CN/install.md b/docs_zh-CN/install.md
@@ -84,40 +84,15 @@ MIM 能够自动地安装 OpenMMLab 的项目以及对应的依赖包。
     pip install mmcv-full
     ```
 
-2. 安装 MMClassification.
+2. 安装 MMClassification 和 MMDetection.
 
-    你可以直接通过如下命令从 pip 安装使用 mmclassification：
+    你可以直接通过如下命令从 pip 安装使用 mmclassification 和 mmdetection：
 
     ```shell
-    pip install mmcls
+    pip install mmcls mmdet
     ```
 
-    或者从 git 仓库编译源码：
-
-    ```shell
-    git clone https://github.com/open-mmlab/mmclassification.git
-    cd mmclassification
-    pip install -r requirements/build.txt
-    python setup.py install
-    ```
-
-3. 安装 MMDetection.
-
-    你可以直接通过如下命令从 pip 安装使用 mmdetection：
-
-    ```shell
-    pip install mmdet
-    ```
-
-    或者从 git 仓库编译源码：
-
-    ```shell
-    git clone https://github.com/open-mmlab/mmdetection.git
-    cd mmdetection
-    pip install -r requirements/build.txt
-    python setup.py install
-    ```
-4. 安装 MMFewShot.
+3. 安装 MMFewShot.
 
     你可以直接通过如下命令从 pip 安装使用 mmfewshot：
 
@@ -170,19 +145,8 @@ conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10
 # 安装最新版本的 mmcv
 pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html
 
-# 安装 mmcls
-git clone https://github.com/open-mmlab/mmclassification.git
-cd mmclassification
-pip install -r requirements/build.txt
-python setup.py install
-cd ..
-
-# 安装 mmdetection
-git clone https://github.com/open-mmlab/mmdetection.git
-cd mmdetection
-pip install -r requirements/build.txt
-python setup.py install
-cd ..
+# 安装 mmclassification mmdetection
+pip install mmcls mmdet
 
 # 安装 mmfewshot
 git clone https://github.com/open-mmlab/mmfewshot.git