From d6b10a1384ec70a346733911795b7a3c331cb21a Mon Sep 17 00:00:00 2001
From: AemikaChow <aem@AemikaChowdeMacBook-Pro.local>
Date: Sat, 31 Oct 2020 23:26:37 +0800
Subject: [PATCH] minor revision

---
 docs/1_exist_data_model.md      | 18 +++++++++---------
 docs/2_new_data_model.md        |  4 ++--
 docs/3_exist_data_new_model.md  |  2 +-
 docs/getting_started.md         |  2 +-
 docs/index.rst                  | 15 ++++++---------
 docs/projects.md                |  2 +-
 docs/{ => tutorials}/config.md  |  2 +-
 docs/tutorials/data_pipeline.md |  2 +-
 docs/tutorials/finetune.md      |  2 +-
 docs/tutorials/index.rst        |  1 +
 10 files changed, 24 insertions(+), 26 deletions(-)
 rename docs/{ => tutorials}/config.md (99%)

diff --git a/docs/1_exist_data_model.md b/docs/1_exist_data_model.md
index e1f5b87b..7a587258 100644
--- a/docs/1_exist_data_model.md
+++ b/docs/1_exist_data_model.md
@@ -1,14 +1,14 @@
-# Case 1: Inference, testing, and training with predefined models and standard datasets
+# 1: Inference, and training with existing models and standard datasets
 
-MMDetection provides hundreds of predefined and pretrained detection models in [Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html)), and supports multiple standard datasets, including Pascal VOC, COCO, CityScapes, LVIS, etc. This note will show how to perform common tasks on these pretrained models and standard datasets, including:
+MMDetection provides hundreds of existing and existing detection models in [Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html)), and supports multiple standard datasets, including Pascal VOC, COCO, CityScapes, LVIS, etc. This note will show how to perform common tasks on these existing models and standard datasets, including:
 
 - Use existing models to inference on given images.
-- Test pretrained models on standard datasets.
+- Test existing models on standard datasets.
 - Train predefined models on standard datasets.
 
-## Inference with pretrained models
+## Inference with existing models
 
-By inference, we mean using trained models to detect objects on images. In MMDetection, a model is defined by a configuration file and pretrained model parameters are save in a checkpoint file.
+By inference, we mean using trained models to detect objects on images. In MMDetection, a model is defined by a configuration file and existing model parameters are save in a checkpoint file.
 
 To start with, we recommend [Faster RCNN](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) with this [configuration file](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) and this [checkpoint file](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth). It is recommended to download the checkpoint file to `checkpoints` directory.
 
@@ -135,11 +135,11 @@ python demo/webcam_demo.py \
     checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
 ```
 
-## Test pretrained models on standard datasets
+## Test existing models on standard datasets
 
 To evaluate a model's accuracy, one usually tests the model on some standard datasets.
 MMDetection supports multiple public datasets including COCO, Pascal VOC, CityScapes, and [more](https://github.com/open-mmlab/mmdetection/tree/master/configs/_base_/datasets).
-This section will show how to test pretrained models on supported datasets.
+This section will show how to test existing models on supported datasets.
 
 ### Prepare datasets
 
@@ -185,7 +185,7 @@ python tools/convert_datasets/cityscapes.py \
 
 TODO: CHANGE TO THE NEW PATH
 
-### Test pretrained models
+### Test existing models
 
 We provide testing scripts for evaluating an existing model on the whole dataset (COCO, PASCAL VOC, Cityscapes, etc.).
 The following testing environments are supported:
@@ -325,7 +325,7 @@ Training requires preparing datasets too. See section [Prepare datasets](#prepar
 
 **Note**:
 Currently, the config files under `configs/cityscapes` use COCO pretrained weights to initialize.
-You could download the pretrained models in advance if the network connection is unavailable or slow. Otherwise, it would cause errors at the beginning of training.
+You could download the existing models in advance if the network connection is unavailable or slow. Otherwise, it would cause errors at the beginning of training.
 
 ### Training on a single GPU
 
diff --git a/docs/2_new_data_model.md b/docs/2_new_data_model.md
index 2ca2a0cc..f56d45a3 100644
--- a/docs/2_new_data_model.md
+++ b/docs/2_new_data_model.md
@@ -1,6 +1,6 @@
-# Case 2: Inference, testing, and training with predefined models and customized datasets
+# 2: Training with existing models and customized datasets
 
-In this case, you will know how to inference, test, and train predefined models with customized datasets. We use the [ballon dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon) as an example to describe the whole process.
+In this note, you will know how to inference, test, and train predefined models with customized datasets. We use the [ballon dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon) as an example to describe the whole process.
 
 The basic steps are as below:
 
diff --git a/docs/3_exist_data_new_model.md b/docs/3_exist_data_new_model.md
index c69424a1..4052d80d 100644
--- a/docs/3_exist_data_new_model.md
+++ b/docs/3_exist_data_new_model.md
@@ -1,4 +1,4 @@
-# Case 3: Inference, testing, and training with predefined models and standard datasets
+# 3: Inference and training with existing models and standard datasets
 
 In this note, you will know how to inference, test, and train predefined models with your own settings for standard datasets. We use the cityscapes dataset to train a COCO pretrained Cascade Mask R-CNN model as an example to describe the whole process.
 
diff --git a/docs/getting_started.md b/docs/getting_started.md
index 380f3c35..e86da5bf 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -214,7 +214,7 @@ To use the default MMDetection installed in the environment rather than that you
 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
 ```
 
-## Verification
+### Verified Installation
 
 To verify whether MMDetection and the required environment are installed correctly, we can run sample python codes to initialize a detector and inference a demo image:
 
diff --git a/docs/index.rst b/docs/index.rst
index 6403219d..8b192651 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -12,7 +12,12 @@ Welcome to MMDetection's documentation!
 
    1_exist_data_model.md
    2_new_data_model.md
-   3_exist_data_new_model.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   tutorials/index.rst
 
 .. toctree::
    :maxdepth: 2
@@ -24,24 +29,16 @@ Welcome to MMDetection's documentation!
    :maxdepth: 2
    :caption: Notes
 
-   config.md
    compatibility.md
    changelog.md
    projects.md
    trouble_shooting.md
 
-.. toctree::
-   :maxdepth: 2
-   :caption: Tutorials
-
-   tutorials/index.rst
-
 .. toctree::
    :caption: API Reference
 
    api.rst
 
-
 Indices and tables
 ==================
 
diff --git a/docs/projects.md b/docs/projects.md
index 2c30902c..4bc4ab7e 100644
--- a/docs/projects.md
+++ b/docs/projects.md
@@ -1,4 +1,4 @@
-# Projects
+# Projects based on MMDetection
 
 There are many projects built upon MMDetection.
 We list some of them as examples of how to extend MMDetection for your own projects.
diff --git a/docs/config.md b/docs/tutorials/config.md
similarity index 99%
rename from docs/config.md
rename to docs/tutorials/config.md
index e561cd92..e4b1a59e 100644
--- a/docs/config.md
+++ b/docs/tutorials/config.md
@@ -1,4 +1,4 @@
-# Config System
+# Tutorial 1: Learn about the Config System
 We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments.
 If you wish to inspect the config file, you may run `python tools/print_config.py /PATH/TO/CONFIG` to see the complete config.
 You may also pass `--cfg-options xxx.yyy=zzz` to see updated config.
diff --git a/docs/tutorials/data_pipeline.md b/docs/tutorials/data_pipeline.md
index 986ae552..02a9b5c8 100644
--- a/docs/tutorials/data_pipeline.md
+++ b/docs/tutorials/data_pipeline.md
@@ -1,4 +1,4 @@
-# Tutorial 3: Custom Data Pipelines
+# Tutorial 3: Customize Data Pipelines
 
 ## Design of Data pipelines
 
diff --git a/docs/tutorials/finetune.md b/docs/tutorials/finetune.md
index 674a0e4d..1c1a97bd 100644
--- a/docs/tutorials/finetune.md
+++ b/docs/tutorials/finetune.md
@@ -1,4 +1,4 @@
-# Tutorial 1: Finetuning Models
+# Tutorial 7: Finetuning Models
 
 Detectors pre-trained on the COCO dataset can serve as a good pre-trained model for other datasets, e.g., CityScapes and KITTI Dataset.
 This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance.
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
index d9c44eea..75cd22a7 100644
--- a/docs/tutorials/index.rst
+++ b/docs/tutorials/index.rst
@@ -1,6 +1,7 @@
 .. toctree::
    :maxdepth: 2
 
+   config.md
    finetune.md
    customize_dataset.md
    data_pipeline.md
-- 
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