[Docs] add reid training README.md

configs/mot/deepsort/README.md

@@ -25,7 +25,7 @@
 
 ## Results and models on MOT17
 
-We implement SORT and DeepSORT with independent detector and ReID models. To train a model by yourself, you need to train a detector following [here](../../det/) and also train a ReID model.
+We implement SORT and DeepSORT with independent detector and ReID models. To train a model by yourself, you need to train a detector following [here](../../det/) and also train a ReID model following [here](../../reid).
 The configs in this folder are basically for inference.
 
 Currently we do not support training ReID models.

configs/reid/README.md

@@ -0,0 +1,144 @@
+# Training a ReID Model
+
+You may want to train a ReID model for multiple object tracking or other applications. We support ReID model training in MMTracking, which is built upon [MMClassification](https://github.com/open-mmlab/mmclassification).
+
+## 1.Standard Dataset
+
+This section will show how to train a ReID model on standard datasets i.e. MOT17.
+
+### Dataset Preparation
+
+We need to download datasets following [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/dataset.md). We use [ReIDDataset](https://github.com/open-mmlab/mmtracking/blob/master/mmtrack/datasets/reid_dataset.py) inherited from [BaseDataset](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/datasets/base_dataset.py) to maintain standard datasets. In this case, you need to convert the official dataset to this style. We provide scripts and the usages as follow:
+
+```python
+python ./tools/convert_datasets/mot2reid.py -i ./data/MOT17/ -o ./data/MOT17/reid --val-split 0.2 --vis-threshold 0.3
+```
+
+Arguments:
+
+- `--val-split`: Proportion of the validation dataset to the whole ReID dataset.
+- `--vis-threshold`: Threshold of visibility for each person.
+
+The directory of the converted datasets is as follows:
+
+```
+MOT17
+├── train
+├── test
+├── reid
+│   ├── imgs
+│   │   ├── MOT17-02-FRCNN_000002
+│   │   │   ├── 000000.jpg
+│   │   │   ├── 000001.jpg
+│   │   │   ├── ...
+│   │   ├── MOT17-02-FRCNN_000003
+│   │   │   ├── 000000.jpg
+│   │   │   ├── 000001.jpg
+│   │   │   ├── ...
+│   ├── meta
+│   │   ├── train_80.txt
+│   │   ├── val_20.txt
+```
+
+Note: `80` in `train_80.txt` means the proportion of the training dataset to the whole ReID dataset is eighty percent. While the proportion of the validation dataset is twenty percent.
+
+For training, we provide a annotation list `train_80.txt`. Each line of the list contrains a filename and its corresponding ground-truth labels. The format is as follows:
+
+```
+MOT17-05-FRCNN_000110/000018.jpg 0
+MOT17-13-FRCNN_000146/000014.jpg 1
+MOT17-05-FRCNN_000088/000004.jpg 2
+MOT17-02-FRCNN_000009/000081.jpg 3
+```
+
+For validation, The annotation list `val_20.txt` remains the same as format above.
+
+Note: Images in `MOT17/reid/imgs` are cropped from raw images in `MOT17/train` by the corresponding `gt.txt`. The value of ground-truth labels should fall in range `[0, num_classes - 1]`.
+
+### Training
+
+#### Training on a single GPU
+
+```shell
+python tools/train.py configs/reid/resnet50_b32*8_MOT17.py [optional arguments]
+```
+
+During training, log files and checkpoints will be saved to the working directory, which is specified by `work_dir` in the config file or via CLI argument `--work-dir`.
+
+#### Training on multiple GPUs
+
+We provide `tools/dist_train.sh` to launch training on multiple GPUs.
+The basic usage is as follows.
+
+```shell
+bash ./tools/dist_train.sh \
+    configs/reid/resnet50_b32*8_MOT17.py \
+    ${GPU_NUM} \
+    [optional arguments]
+```
+
+Optional arguments remain the same as stated above.
+
+For more training details, please refer to [here](https://github.com/open-mmlab/mmtracking/blob/master/docs/quick_run.md).
+
+## 2.Customize Dataset
+
+This section will show how to train a ReID model on customize datasets.
+
+### Dataset Preparation
+
+You need to convert your customize datasets to existing dataset format.
+
+#### An example of customized dataset
+
+Assume we are going to implement a `Filelist` dataset, which takes filelists for both training and testing. The directory of the dataset is as follows:
+
+```
+Filelist
+├── imgs
+│   ├── person1
+│   │   ├── 000000.jpg
+│   │   ├── 000001.jpg
+│   │   ├── ...
+│   ├── person2
+│   │   ├── 000000.jpg
+│   │   ├── 000001.jpg
+│   │   ├── ...
+├── meta
+│   ├── train.txt
+│   ├── val.txt
+```
+
+The format of annotation list is as follows:
+
+```
+person1/000000.jpg 0
+person1/000001.jpg 0
+person2/000000.jpg 1
+person2/000001.jpg 1
+```
+
+You can directly use [ReIDDataset](https://github.com/open-mmlab/mmtracking/blob/master/mmtrack/datasets/reid_dataset.py). In this case, you only need to modify the config as follows:
+
+```python
+# modify the path of annotation files and the image path prefix
+data = dict(
+    train=dict(
+        data_prefix='data/Filelist/imgs',
+        ann_file='data/Filelist/meta/train.txt'),
+    val=dict(
+        data_prefix='data/Filelist/imgs',
+        ann_file='data/Filelist/meta/val.txt'),
+    test=dict(
+        data_prefix='data/Filelist/imgs',
+        ann_file='data/Filelist/meta/val.txt'),
+)
+# modify the number of classes, assume your training set has 100 classes
+model = dict(reid=dict(head=dict(num_classes=100)))
+```
+
+You also can write a new Dataset class inherited from `BaseDataset`, and overwrite `load_annotations(self)`. For more details, you can follow [here](https://github.com/open-mmlab/mmclassification/blob/master/docs/tutorials/new_dataset.md).
+
+### Training
+
+The training stage is the same as `Standard Dataset`.

docs_zh-CN/install.md

@@ -40,7 +40,7 @@
     ```shell
     conda install pytorch==1.5 cudatoolkit=10.1 torchvision -c pytorch
     ```
-    
+
     `例 2` 例如在 `/usr/local/cuda` 安装了 CUDA 9.2，并想安装 PyTorch 1.3.1，则需要安装支持 CUDA 9.2 的预构建 PyTorch：
 
     ```shell
@@ -69,7 +69,7 @@
     ```shell
     pip install mmcv-full
     ```
-    
+
 4. 安装 MMDetection：
 
     ```shell
@@ -84,14 +84,14 @@
     pip install -r requirements/build.txt
     pip install -v -e .  # or "python setup.py develop"
     ```
-    
+
 5. 将 MMTracking 仓库克隆到本地：
 
     ```shell
     git clone https://github.com/open-mmlab/mmtracking.git
     cd mmtracking
     ```
-    
+
 6. 首先安装依赖，然后安装 MMTracking：
 
     ```shell