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Prepare datasets for GTR

Our MOT models are trained on MOT17 and CrowdHuman and are evaluated on MOT17. Our TAO models are trained on LVIS and COCO images), and evaluated on TAO.

Before starting processing, please download the datasets from the official websites and place or sim-link them under $Detic_ROOT/datasets/.

$Detic_ROOT/datasets/
    lvis/
    coco/
    mot/
    crowdhuman/
    tao/

Please follow the following instruction to pre-process individual datasets.

metadata/ is our preprocessed meta-data (included in the repo). See the below section for details. Please follow the following instruction to pre-process individual datasets.

MOT

First, download and place them in the following way

mot/
    MOT17/
        train/
            MOT17-02-FRCNN/
            ...
        test/
            MOT17-01-FRCNN/
            ...

Then create sim-link to facilitate our evaluation script

cd datasets/mot/MOT17/
ln -s train trainval
cd ../../../

Then create the half-half train/ validation split and convert the annotation format

python tools/convert_mot2coco.py

This creates datasets/mot/MOT17/annotations/train_half_conf0.json and datasets/mot/MOT17/annotations/val_half_conf0.json. Note that these files are different from CenterTrack as CenterTrack filters annotations with a visibility threshold of 0.25.

To generate the annotation files for the train/test split, change the SPLITS in tools/convert_mot2coco.py to SPLITS = ['train', 'test'] and run python tools/convert_mot2coco.py again.

Crowdhuman

Download the data and place them as the following:

crowdhuman/
    CrowdHuman_train/
        Images/
    CrowdHuman_val/
        Images/
    annotation_train.odgt
    annotation_val.odgt

Convert the annotation format by

python tools/convert_crowdhuman_amodal.py

This creates datasets/crowdhuman/annotations/train_amodal.json and datasets/crowdhuman/annotations/train_amodal.json.

COCO and LVIS

Download COCO and LVIS data place them in the following way:

lvis/
    lvis_v1_train.json
    lvis_v1_val.json
coco/
    train2017/
    val2017/
    annotations/
        captions_train2017.json
        instances_train2017.json 
        instances_val2017.json

Next, prepare the merged annotation file using

python tools/merge_lvis_coco.py

This creates datasets/lvis/lvis_v1_train+coco_box.json

TAO

Download the data following the official instructions and place them as

tao/
    frames/
        val/
            ArgoVerse/
            AVA/
            BDD/
            Charades/
            HACS/
            LaSOT/
            TFCC100M/
        train/
            ArgoVerse/
            ...
        test/
            ArgoVerse/
            ...
    annotations/
        train.json
        validation.json
        test_without_annotations.json

Our model only uses the annotated frames ("keyframe"). To make the data management easier, we first copy the keyframes to a new folder

python tools/move_tao_keyframes.py --gt datasets/tao/annotations/validation.json --img_dir datasets/tao/frames --img_dir datasets/tao/keyframes

This creates tao/keyframes/

The TAO annotations are originally based on LVIS v0.5. We update them to LVIS v1 for validation.

python tools/create_tao_v1.py datasets/tao/annotations/validation.json

This creates datasets/tao/annotations/validation_v1.json.

For TAO test set, we'll convert the LVIS v1 labels back to v0.5 for the server-based test set evaluation.