metadata

annotations_creators: []
language: en
license: cc-by-sa-3.0
task_categories:
  - object-detection
task_ids: []
pretty_name: VisDrone2019-DET
tags:
  - fiftyone
  - image
  - object-detection
dataset_summary: >



  ![image/png](dataset_preview.jpg)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8629
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  import fiftyone.utils.huggingface as fouh


  # Load the dataset dataset = fouh.load_from_hub("Voxel51/VisDrone2019-DET")

  ## Or just load the first 1000 samples ## dataset =
  fouh.load_from_hub("Voxel51/VisDrone2019-DET", max_samples=1000)


  # Launch the App

  session = fo.launch_app(dataset)

  ```
size_categories:
  - 1K<n<10K

Dataset Card for VisDrone2019-DET

This is a FiftyOne version of the VisDrone2019-DET dataset with 8629 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', 'persistent`, 'overwrite' etc
dataset = fouh.load_from_hub("Voxel51/VisDrone2019-DET")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

Curated by: AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China
Language(s) (NLP): en
License: cc-by-sa-3.0

Dataset Sources

Repository: https://github.com/VisDrone/VisDrone-Dataset
Paper: Detection and Tracking Meet Drones Challenge

Dataset Structure

Name:        VisDrone2019-DET
Media type:  image
Num samples: 8629
Persistent:  False
Tags:        []
Sample fields:
    id:           fiftyone.core.fields.ObjectIdField
    filepath:     fiftyone.core.fields.StringField
    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)

The dataset has 3 splits: "train", "val", and "test". Samples are tagged with their split.

Dataset Creation

Created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China.

Source Data

Who are the source data producers?

The VisDrone Dataset is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. It contains carefully annotated ground truth data for various computer vision tasks related to drone-based image and video analysis.

Personal and Sensitive Information

The authors of the dataset have done their best to exclude identifiable information from the data to protect privacy. If you find your vehicle or personal information in this dataset, please contact them and they will remove the corresponding information from their dataset. They are not responsible for any actual or potential harm as the result of using this dataset.

Citation

BibTeX:

@ARTICLE{9573394,
  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={Detection and Tracking Meet Drones Challenge},
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3119563}}

Copyright Information

The copyright of the VisDrone dataset is reserved by the AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China. The dataset described on this page is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which implies that you must: (1) attribute the work as specified by the original authors; (2) may not use this work for commercial purposes ; (3) if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. The dataset is provided “as it is” and we are not responsible for any subsequence from using this dataset.