dataset.md

Dataset Preparation

This page provides the instructions for dataset preparation on existing benchmarks, include

• Video Object Detection
  • ILSVRC
• Multiple Object Tracking
  • MOT Challenge
• Single Object Tracking
  • LaSOT
  • UAV123
  • TrackingNet
  • OTB100

1. Download Datasets

Please download the datasets from the offical websites. It is recommended to symlink the root of the datasets to $MMTRACKING/data. If your folder structure is different from the following, you may need to change the corresponding paths in config files.

For OTB100 dataset, you don't need to download the dataset from the official website manually, since we provide a sctipt to download it.

OTB100

# download OTB100 dataset by web crawling
python ./tools/convert_datasets/otb100/download_otb100.py -o ./data/otb100/zips -p 8

Notes:

• The Lists under ILSVRC contains the txt files from here.

• For the training and testing of video object detection task, only ILSVRC dataset is needed.

• For the training and testing of multi object tracking task, only one of the MOT Challenge dataset (e.g. MOT17) is needed.

• For the training and testing of single object tracking task, the MSCOCO, ILSVRC, LaSOT, UAV123, TrackingNet and OTB100 datasets are needed.

mmtracking
├── mmtrack
├── tools
├── configs
├── data
│   ├── coco
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017
│   │   ├── annotations
│   │
│   ├── ILSVRC
│   │   ├── Data
│   │   │   ├── DET
|   │   │   │   ├── train
|   │   │   │   ├── val
|   │   │   │   ├── test
│   │   │   ├── VID
|   │   │   │   ├── train
|   │   │   │   ├── val
|   │   │   │   ├── test
│   │   ├── Annotations
│   │   │   ├── DET
|   │   │   │   ├── train
|   │   │   │   ├── val
│   │   │   ├── VID
|   │   │   │   ├── train
|   │   │   │   ├── val
│   │   ├── Lists
│   │
│   ├── lasot
│   │   ├── LaSOTTesting
│   │   │   ├── airplane-1
│   │   │   ├── airplane-13
│   │
|   ├── MOT15/MOT16/MOT17/MOT20
|   |   ├── train
|   |   ├── test
│   │
│   ├── UAV123
│   │   ├── data_seq
│   │   │   ├── UAV123
│   │   │   │   ├── bike1
│   │   │   │   ├── boat1
│   │   ├── anno
│   │   │   ├── UAV123
│   │
│   ├── trackingnet
│   │   ├── TEST
│   │   │   ├── anno
│   │   │   ├── zips
│   │
│   ├── otb100
│   │   │── zips
│   │   │   │── Basketball.zip
│   │   │   │── Biker.zip

2. Convert Annotations

We use CocoVID to maintain all datasets in this codebase. In this case, you need to convert the offical annotations to this style. We provide scripts and the usages are as following:

# ImageNet DET
python ./tools/convert_datasets/ilsvrc/imagenet2coco_det.py -i ./data/ILSVRC -o ./data/ILSVRC/annotations

# ImageNet VID
python ./tools/convert_datasets/ilsvrc/imagenet2coco_vid.py -i ./data/ILSVRC -o ./data/ILSVRC/annotations

# LaSOT
python ./tools/convert_datasets/lasot/lasot2coco.py -i ./data/lasot/LaSOTTesting -o ./data/lasot/annotations

# MOT17
# The processing of other MOT Challenge dataset is the same as MOT17
python ./tools/convert_datasets/mot/mot2coco.py -i ./data/MOT17/ -o ./data/MOT17/annotations --split-train --convert-det
python ./tools/convert_datasets/mot/mot2reid.py -i ./data/MOT17/ -o ./data/MOT17/reid --val-split 0.2 --vis-threshold 0.3

# UAV123
python ./tools/convert_datasets/uav123/uav2coco.py -i ./data/UAV123/ -o ./data/UAV123/annotations

# TrackingNet
# unzip files in 'data/trackingnet/TEST/zips/*.zip'
bash ./tools/convert_datasets/trackingnet/unzip_trackingnet_test.sh ./data/trackingnet/TEST
# generate testset annotaions
python ./tools/convert_datasets/trackingnet/trackingnet2coco.py -i ./data/trackingnet/TEST/ -o ./data/trackingnet/TEST/annotations

# OTB100
# unzip files in 'data/otb100/zips/*.zip'
bash ./tools/convert_datasets/otb100/unzip_otb100.sh ./data/otb100
# generate annotations
python ./tools/convert_datasets/otb100/otb2coco.py -i ./data/otb100/data -o ./data/otb100/annotations

The folder structure will be as following after your run these scripts:

mmtracking
├── mmtrack
├── tools
├── configs
├── data
│   ├── coco
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017
│   │   ├── annotations
│   │
│   ├── ILSVRC
│   │   ├── Data
│   │   │   ├── DET
|   │   │   │   ├── train
|   │   │   │   ├── val
|   │   │   │   ├── test
│   │   │   ├── VID
|   │   │   │   ├── train
|   │   │   │   ├── val
|   │   │   │   ├── test
│   │   ├── Annotations (the offical annotation files)
│   │   │   ├── DET
|   │   │   │   ├── train
|   │   │   │   ├── val
│   │   │   ├── VID
|   │   │   │   ├── train
|   │   │   │   ├── val
│   │   ├── Lists
│   │   ├── annotations (the converted annotation files)
│   │
│   ├── lasot
│   │   ├── LaSOTTesting
│   │   │   ├── airplane-1
│   │   │   ├── airplane-13
│   │   ├── annotations
│   │
|   ├── MOT15/MOT16/MOT17/MOT20
|   |   ├── train
|   |   ├── test
|   |   ├── annotations
|   |   ├── reid
│   │   │   ├── imgs
│   │   │   ├── meta
│   │
│   ├── UAV123
│   │   ├── data_seq
│   │   │   ├── UAV123
│   │   │   │   ├── bike1
│   │   │   │   ├── boat1
│   │   ├── anno (the offical annotation files)
│   │   │   ├── UAV123
│   │   ├── annotations (the converted annotation file)
│   │
│   ├── trackingnet
│   │   ├── TEST
│   │   │   ├── anno (the offical annotation files)
│   │   │   ├── zips
│   │   │   ├── annotations (the converted annotation file)
│   │   │   ├── frames (the unzipped folders)
│   │   │   │   ├── 0-6LB4FqxoE_0
│   │   │   │   ├── 07Ysk1C0ZX0_0
│   │
│   ├── otb100
│   │   ├── zips
│   │   │   ├── Basketball.zip
│   │   │   ├── Biker.zip
│   │   ├── annotations
│   │   ├── data
│   │   │   ├── Basketball
│   │   │   │   ├── img

The folder of annotations in ILSVRC

There are 3 json files in data/ILSVRC/annotations:

imagenet_det_30plus1cls.json: Json file containing the annotations information of the training set in ImageNet DET dataset. The 30 in 30plus1cls denotes the overlapped 30 categories in ImageNet VID dataset, and the 1cls means we take the other 170 categories in ImageNet DET dataset as a category, named as other_categeries.

imagenet_vid_train.json: Json file containing the annotations information of the training set in ImageNet VID dataset.

imagenet_vid_val.json: Json file containing the annotations information of the validation set in ImageNet VID dataset.

The folder of annotations in lasot

There are only 1 json files in data/lasot/annotations:

lasot_test.json: Json file containing the annotations information of the testing set in LaSOT dataset.

The folder of annotations and reid in MOT15/MOT16/MOT17/MOT20

We take MOT17 dataset as examples, the other datasets share similar struture.

There are 8 json files in data/MOT17/annotations:

train_cocoformat.json: Json file containing the annotations information of the training set in MOT17 dataset.

train_detections.pkl: Pickle file containing the public detections of the training set in MOT17 dataset.

test_cocoformat.json: Json file containing the annotations information of the testing set in MOT17 dataset.

test_detections.pkl: Pickle file containing the public detections of the testing set in MOT17 dataset.

half-train_cocoformat.json, half-train_detections.pkl, half-val_cocoformat.jsonand half-val_detections.pkl share similar meaning with train_cocoformat.json and train_detections.pkl. The half means we split each video in the training set into half. The first half videos are denoted as half-train set, and the second half videos are denoted ashalf-val set.

The struture of data/MOT17/reid is as follows:

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

The 80 in train_80.txt means the proportion of the training dataset to the whole ReID dataset is 80%. While the proportion of the validation dataset is 20%.

For training, we provide a annotation list train_80.txt. Each line of the list contains 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

MOT17-05-FRCNN_000110 denotes the 110-th person in MOT17-05-FRCNN video.

For validation, The annotation list val_20.txt remains the same as format above.

Images in 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].

The folder of annotations in UAV123

There are only 1 json files in data/UAV123/annotations:

uav123.json: Json file containing the annotations information of the UAV123 dataset.

The folder of frames and annotations in TrackingNet

There are 511 video directories of TrackingNet testset in data/trackingnet/TEST/frames, and each video directory contains all images of the video.

There are only 1 json files in data/trackingnet/TEST/annotations:

trackingnet_test.json: Json file containing the annotations information of the testing set in TrackingNet dataset.

The folder of data and annotations in OTB100

There are 98 video directories of OTB100 dataset in data/otb100/data, and the img folder under each video directory contains all images of the video.

There are only 1 json files in data/otb100/annotations:

otb100.json: Json file containing the annotations information of the OTB100 dataset.