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Data Preparation

We put pretrained backbone weights under ${UNICORN_ROOT} and put all data under the datasets folder. The complete data structure looks like this.

${UNICORN_ROOT}
    -- convnext_tiny_1k_224_ema.pth
    -- convnext_large_22k_224.pth
    -- datasets
        -- bdd
            -- images
                -- 10k
                -- 100k
                -- seg_track_20
                -- track
            -- labels
                -- box_track_20
                -- det_20
                -- ins_seg
                -- seg_track_20
        -- Cityscapes
            -- annotations
            -- images
            -- labels_with_ids
        -- COCO
            -- annotations
            -- train2017
            -- val2017
        -- crowdhuman
            -- annotations
            -- CrowdHuman_train
            -- CrowdHuman_val
            -- annotation_train.odgt
            -- annotation_val.odgt
        -- DAVIS
            -- Annotations
            -- ImageSets
            -- JPEGImages
            -- README.md
            -- SOURCES.md
        -- ETHZ
            -- annotations
            -- eth01
            -- eth02
            -- eth03
            -- eth05
            -- eth07
        -- GOT10K
            -- test
                -- GOT-10k_Test_000001
                -- ...
            -- train
                -- GOT-10k_Train_000001
                -- ...
        -- LaSOT
            -- airplane
            -- basketball
            -- ...
        -- mot
            -- annotations
            -- test
            -- train
        -- MOTS
            -- annotations
            -- test
            -- train
        -- saliency
            -- image
            -- mask
        -- TrackingNet
            -- TEST
            -- TRAIN_0
            -- TRAIN_1
            -- TRAIN_2
            -- TRAIN_3
        -- ytbvos18
            -- train
            -- val

Pretrained backbone weights

Unicorn uses ConvNeXt as the backbone by default. The pretrained backbone weights can be downloaded by the following commands.

wget -c https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth # convnext-tiny
wget -c https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth # convnext-large

Data

For users who are only interested in part of tasks, there is no need of downloading all datasets. The following lines list the datasets needed for different tasks.

  • Object detection & instance segmentation: COCO
  • SOT: COCO, LaSOT, GOT-10K, TrackingNet
  • VOS: DAVIS, Youtube-VOS 2018, COCO, Saliency
  • MOT & MOTS (MOT Challenge 17, MOTS Challenge): MOT17, CrowdHuman, ETHZ, CityPerson, COCO, MOTS
  • MOT & MOTS (BDD100K): BDD100K

Object Detection & Instance Segmentation

Please download COCO from the offical website. We use train2017.zip, val2017.zip & annotations_trainval2017.zip. We expect that the data is organized as below.

${UNICORN_ROOT}
    -- datasets
        -- COCO
            -- annotations
            -- train2017
            -- val2017

SOT

Please download COCO, LaSOT, GOT-10K and TrackingNet. Since TrackingNet is very large and hard to download, we only use the first 4 splits (TRAIN_0.zip, TRAIN_1.zip, TRAIN_2.zip, TRAIN_3.zip) rather than the complete 12 splits for the training set. The original TrackingNet zips (put under datasets) can be unzipped by the following commands.

python3 tools/process_trackingnet.py

We expect that the data is organized as below.

${UNICORN_ROOT}
    -- datasets
        -- COCO
            -- annotations
            -- train2017
            -- val2017
        -- GOT10K
            -- test
                -- GOT-10k_Test_000001
                -- ...
            -- train
                -- GOT-10k_Train_000001
                -- ...
        -- LaSOT
            -- airplane
            -- basketball
            -- ...
        -- TrackingNet
            -- TEST
            -- TRAIN_0
            -- TRAIN_1
            -- TRAIN_2
            -- TRAIN_3

VOS

Please download DAVIS, Youtube-VOS 2018, COCO, Saliency. The saliency dataset is constructed from DUTS, DUT-OMRON, etc. The downloaded youtube-vos zips can be processed using the following commands.

unzip -qq ytbvos18_train.zip
unzip -qq ytbvos18_val.zip
mkdir ytbvos18
mv train ytbvos18/train
mv valid ytbvos18/val
rm -rf ytbvos18_train.zip
rm -rf ytbvos18_val.zip
mv ytbvos18 datasets

We expect that the data is organized as below.

${UNICORN_ROOT}
    -- datasets
        -- COCO
            -- annotations
            -- train2017
            -- val2017
        -- DAVIS
            -- Annotations
            -- ImageSets
            -- JPEGImages
            -- README.md
            -- SOURCES.md
        -- saliency
            -- image
            -- mask
        -- ytbvos18
            -- train
            -- val

MOT & MOTS (MOT Challenge)

Download MOT17, CrowdHuman, Cityperson, ETHZ, MOTS and put them under datasets in the following structure:

${UNICORN_ROOT}
    -- datasets
        -- Cityscapes
            -- annotations
            -- images
            -- labels_with_ids
        -- COCO
            -- annotations
            -- train2017
            -- val2017
        -- crowdhuman
            -- annotations
            -- CrowdHuman_train
            -- CrowdHuman_val
            -- annotation_train.odgt
            -- annotation_val.odgt
        -- ETHZ
            -- annotations
            -- eth01
            -- eth02
            -- eth03
            -- eth05
            -- eth07
        -- mot
            -- annotations
            -- test
            -- train
        -- MOTS
            -- annotations
            -- test
            -- train

unzip CityPersons dataset by

cat Citypersons.z01 Citypersons.z02 Citypersons.z03 Citypersons.zip > c.zip
zip -FF Citypersons.zip --out c.zip
unzip -qq c.zip

Unzip CrowdHuman dataset by

# unzip the train split
unzip -qq CrowdHuman_train01.zip
unzip -qq CrowdHuman_train02.zip
unzip -qq CrowdHuman_train03.zip
mv Images CrowdHuman_train
# unzip the val split
unzip -qq CrowdHuman_val.zip
mv Images CrowdHuman_val

Then, you need to turn the datasets to COCO format:

python3 tools/convert_mot17_to_coco.py
python3 tools/convert_mot17_to_omni.py --dataset_name mot
python3 tools/convert_crowdhuman_to_coco.py
python3 tools/convert_cityperson_to_coco.py
python3 tools/convert_ethz_to_coco.py
python3 tools/convert_mots_to_coco.py

MOT & MOTS (BDD100K)

We need to download the detection set, tracking set, instance seg set and tracking & seg set for training and validation. For more details about the dataset, please refer to the offial documentation.

We provide the following commands to download and process BDD100K datasets in parallel.

cd external/qdtrack
python3 download_bdd100k.py # replace save_dir to your path
bash prepare_bdd100k.sh # replace paths to yours
ln -s <UNICORN_ROOT>/external/qdtrack/data/bdd <UNICORN_ROOT>/datasets/bdd

We expect that the data is organized as below

${UNICORN_ROOT}
    -- datasets
        -- bdd
            -- images
                -- 10k
                -- 100k
                -- seg_track_20
                -- track
            -- labels
                -- box_track_20
                -- det_20
                -- ins_seg
                -- seg_track_20