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The COCO-Tasks Dataset

This repository contains the COCO-Tasks Dataset. For more information regarding the dataset please check the our paper.

If you use our dataset, please cite our paper.

@inproceedings{cocotasks2019,
    Author    = {Sawatzky, Johann and Souri, Yaser and Grund, Christian and Gall, Juergen},
    Title     = {{What Object Should I Use? - Task Driven Object Detection}},
    Booktitle = {{CVPR}},
    Year      = {2019}
}

Table of Contents

• Downloading
  • CVPR 2019 Release
• Usage
  • Annotations
  • Image Lists
  • Detection Files

Downloading

NOTE: We use Git-LFS to host the annotation files, so you need to first install it.

Then you can clone our repository which will download all the files.

CVPR 2019 Release

To download our dataset release used in our CVPR 2019 paper, use the following:

git clone -b cvpr2019 --depth 1 git@github.com:coco-tasks/dataset.git coco-tasks

Usage

The dataset's annotation format is the same as the COCO format with a small addition. This means that you can use the COCO's API to use our dataset.

You also have to download the images separately from COCO dataset's website.

If you want to reproduce our results, checkout our code: yassersouri/task-driven-object-detection

Annotations

Our annotations are located in the annotations folder. For example the file task_1_train.json is the training annotation file for task 1.

Each annotation, i.e. an object in an image is specified using the following dictionary structure in the JSON files.

{
    'segmentation': [], // the segmentation mask of the object
    'area': 33356.1888,
    'iscrowd': 0,
    'image_id': 576993,  // COCO image id of the object
    'bbox': [57.6, 87.65, 266.4, 195.12],  // bounding box of the object
    'COCO_category_id': 58,  // the original category id from COCO
    'category_id': 0,  // whether or not it is preferred object in this image for the task. 0 means "not preferred" and 1 means "preferred"
    'id': 207 // COCO annotation id of the object
}

Image Lists

The text files inside the image_lists directory specify which COCO image ids where used for the creation of our dataset.

There is no need to use these files, they are here for the sake of completeness.

Detection Files

As described in our paper, at test time, we need to first perform object detection on an image. For reproducibility purposes, we have included the result of object detection from our custom trained Faster-RCNN and YOLOv2 object detectors in detections_faster.json and detections_yolo.json files respectively.

Each detection is specified using the following dictionary structed in the JSON file.

{
    'image_id': 262148, //COCO image id
    'category_id': 1,  //COCO category id
    'bbox': [250.77206420898438,  // bounding box of the detection [x, y, width, height]
             50.91807556152344,
             154.542236328125,
             295.10801696777344],
    'score': 0.9993196725845337  // detection score in the range [1, 0]
}

LICENSE

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.