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docs/en/migration.md

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 # Migration from MMSegmentation 0.x
+
+## Introduction
+
+This guide describes the fundamental differences between MMSegmentation 0.x and MMSegmentation 1.x in terms of behaviors and the APIs, and how these all relate to your migration journey.
+
+## New dependencies
+
+MMSegmentation 1.x depends on some new packages, you can prepare a new clean environment and install again according to the [installation tutorial](get_started.md).
+Or install the below packages manually.
+
+1. [MMEngine](https://github.com/open-mmlab/mmengine): MMEngine is the core the OpenMMLab 2.0 architecture, and we splited many compentents unrelated to computer vision from MMCV to MMEngine.
+
+2. [MMCV](https://github.com/open-mmlab/mmcv): The computer vision package of OpenMMLab. This is not a new dependency, but you need to upgrade it to above 2.0.0rc1 version.
+
+3. [MMClassification](https://github.com/open-mmlab/mmclassification)(Optional): The  image classification toolbox and benchmark of OpenMMLab. This is not a new dependency, but you need to upgrade it to above 1.0.0rc0 version.
+
+## Train launch
+
+The main improvement of OpenMMLab 2.0 is releasing MMEngine which provides universal and powerful runner for unified interfaces to launch training jobs.
+
+Compared with MMSeg0.x, MMSeg1.x provides fewer command line arguments in `tools/train.py`
+
+<table class="docutils">
+<tr>
+<td>Function</td>
+<td>Original</td>
+<td>New</td>
+</tr>
+<tr>
+<td>Train configuration file path</td>
+<td>args.config</td>
+<td>args.config</td>
+</tr>
+<tr>
+<td>Directory to save logs and models</td>
+<td>args.work_dir</td>
+<td>args.work_dir</td>
+</tr>
+<tr>
+<td>Loading pre-trained checkpoint</td>
+<td>`args.load_from` or `cfg.load_from`</td>
+<td>cfg.load_from</td>
+</tr>
+<tr>
+<td>Resuming Train</td>
+<td></td>
+</tr>
+<tr>
+<td>Whether not to evaluate the checkpoint during training</td>
+</tr>
+<tr>
+<td>Training device assignment</td>
+</tr>
+
+</table>
+
+## Configuration file
+
+### Model settings
+
+No changes in `model.backbone`, `model.neck`, `model.decode_head` and `model.losses` fields.
+
+Add `model.data_preprocessor` field to configure the `DataPreProcessor`, including:
+
+- `mean`(Sequence, optional): The pixel mean of R, G, B channels. Defaults to None.
+
+- `std`(Sequence, optional): The pixel standard deviation of R, G, B channels. Defaults to None.
+
+- `size`(Sequence, optional): Fixed padding size.
+
+- `size_divisor` (int, optional): The divisor of padded size.
+
+- `seg_pad_val` (float, optional): Padding value of segmentation map. Default: 255.
+
+- `padding_mode` (str): Type of padding. Default: constant.
+
+  - constant: pads with a constant value, this value is specified with pad_val.
+
+- `bgr_to_rgb` (bool): whether to convert image from BGR to RGB.Defaults to False.
+
+- `rgb_to_bgr` (bool): whether to convert image from RGB to RGB. Defaults to False.
+
+### Data settings
+
+### Optimizer and Schedule settings
+
+### Runtime settings