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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.

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. We use ReIDDataset inherited from BaseDataset 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 ./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

python tools/train.py configs/reid/resnet50_b32x8_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.

bash ./tools/dist_train.sh \
    configs/reid/resnet50_b32x8_MOT17.py \
    ${GPU_NUM} \
    [optional arguments]

Optional arguments remain the same as stated above.

For more training details, please refer to here.

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. In this case, you only need to modify the config as follows:

# 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.

Training

The training stage is the same as Standard Dataset.