Voxel51/VisDrone2019-DET
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… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/VisDrone2019-DET.
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 fiftyoneUsage
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
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- 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
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- 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.
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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.
