added installation and fixed some formatting

docs/getting_started.md

 # Getting Started
 
 This page provides basic tutorials about the usage of MMDetection.
-For installation instructions, please see [install.md](install.md).
+
+## Installation
+
+### Install mmdetection
+
+1. Create a conda virtual environment and activate it.
+
+    ```shell
+    conda create -n open-mmlab python=3.7 -y
+    conda activate open-mmlab
+    ```
+
+2. Install PyTorch and torchvision following the [official instructions
+](https://pytorch.org/), e.g.,
+
+    ```shell
+    conda install pytorch torchvision -c pytorch
+    ```
+
+    Note: Make sure that your compilation CUDA version and runtime CUDA version match.
+    You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/).
+
+    `E.g.1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install
+    PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.
+
+    ```shell
+    conda install pytorch cudatoolkit=10.1 torchvision -c pytorch
+    ```
+
+    `E.g. 2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install
+    PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.
+
+    ```shell
+    conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
+    ```
+
+    If you build PyTorch from source instead of installing the prebuilt pacakge,
+    you can use more CUDA versions such as 9.0.
+
+3. Install mmcv, we recommend you to install the pre-build mmcv as below.
+
+    ```shell
+    pip install mmcv-full==latest+torch1.5.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html
+    ```
+
+    See [here](https://github.com/open-mmlab/mmcv#install-with-pip) for different versions of MMCV compatible to different PyTorch and CUDA versions.
+    Optionally you can choose to compile mmcv from source by the following command
+
+    ```shell
+    git clone https://github.com/open-mmlab/mmcv.git
+    cd mmcv
+    MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full will be installed after this step
+    cd ..
+    ```
+
+    Or directly run
+
+    ```shell
+    pip install mmcv-full
+    ```
+
+4. Clone the mmdetection repository.
+
+    ```shell
+    git clone https://github.com/open-mmlab/mmdetection.git
+    cd mmdetection
+    ```
+
+5. Install build requirements and then install mmdetection.
+(We install our forked version of pycocotools via the github repo instead of pypi
+for better compatibility with our repo.)
+
+    ```shell
+    pip install -r requirements/build.txt
+    pip install -v -e .  # or "python setup.py develop"
+    ```
+
+    If you build mmdetection on macOS, replace the last command with
+
+    ```shell
+    CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .
+    ```
+
+Note:
+
+a. The git commit id will be written to the version number with step 4, e.g
+. 0.6.0+2e7045c. The version will also be saved in trained models.
+It is recommended that you run step 4 each time you pull some updates from
+ github. If C++/CUDA codes are modified, then this step is compulsory.
+
+    > Important: Be sure to remove the `./build` folder if you reinstall mmdet with a different CUDA/PyTorch version.
+
+    ```shell
+    pip uninstall mmdet
+    rm -rf ./build
+    find . -name "*.so" | xargs rm
+    ```
+
+b. Following the above instructions, mmdetection is installed on `dev` mode
+, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
+
+c. If you would like to use `opencv-python-headless` instead of `opencv
+-python`,
+you can install it before installing MMCV.
+
+d. Some dependencies are optional. Simply running `pip install -v -e .` will
+ only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.
+
+### Install with CPU only
+
+The code can be built for CPU only environment (where CUDA isn't available).
+
+In CPU mode you can run the demo/webcam_demo.py for example.
+However some functionality is gone in this mode:
+
+- Deformable Convolution
+- Deformable ROI pooling
+- CARAFE: Content-Aware ReAssembly of FEatures
+- nms_cuda
+- sigmoid_focal_loss_cuda
+
+So if you try to run inference with a model containing deformable convolution you will get an error.
+Note: We set `use_torchvision=True` on-the-fly in CPU mode for `RoIPool` and `RoIAlign`
+
+### Another option: Docker Image
+
+We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image.
+
+```shell
+# build an image with PyTorch 1.5, CUDA 10.1
+docker build -t mmdetection docker/
+```
+
+Run it with
+
+```shell
+docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection
+```
+
+### A from-scratch setup script
+
+Here is a full script for setting up mmdetection with conda.
+
+```shell
+conda create -n open-mmlab python=3.7 -y
+conda activate open-mmlab
+
+# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
+conda install -c pytorch pytorch torchvision -y
+
+# install the latest mmcv
+pip install mmcv-full
+
+# install mmdetection
+git clone https://github.com/open-mmlab/mmdetection.git
+cd mmdetection
+pip install -r requirements/build.txt
+pip install -v -e .
+```
+
+### Using multiple MMDetection versions
+
+The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMDetection in the current directory.
+
+To use the default MMDetection installed in the environment rather than that you are working with, you can remove the following line in those scripts
+
+```shell
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
+```
+
 
 ## Prepare datasets