Visdrone Dataset

Name: VisDrone Dataset
Creator: Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin
License: https://creativecommons.org/publicdomain/zero/1.0/

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Visualization of the Visdrone -DET dataset in the Deep Lake UI

Visdrone Dataset

What is Visdrone Dataset?

The AISKYEYE team at Tianjin University Lab of Machine Learning and Data Mining has gathered the data for the VisDrone benchmark dataset. It is designed to promote the integration of vision and drones.

The benchmark dataset consists of 288 video clips composed of 261,908 frames and 10,209 static photos collected by several drone-mounted cameras, encompassing a wide variety of features such as location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, automobiles, bicycles, etc.), and density (sparse and crowded scenes). It should be noted that the dataset was gathered utilizing a variety of drone platforms (i.e., drones of various types), in a variety of settings, and under a variety of weather and lighting circumstances.

Download Visdrone-DET Dataset in Python

Instead of downloading the Visdrone-DET dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load Visdrone-DET Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/visdrone-det-train")

Load Visdrone-DET Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/visdrone-det-test")

Load Visdrone-DET Dataset Validation Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/visdrone-det-val")

Load Visdrone-DET Dataset Testing-DEV Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/visdrone-det-test-dev")

Visdrone-DET Dataset Structure

Visdrone-DET Data Fields

image: tensor containing the image
label: tensor representing the object detected.
boxes: tensor representing bounding box for the object of interest

Visdrone-DET Data Splits

The Visdrone-DET training split comprises 6471 images.
The Visdrone-DET testing split comprises 548 images.
Visdrone-DET validation split comprises 1580 images.
Visdrone-DET test-dev split comprises 1610 images.

How to use Visdrone-DET Dataset with PyTorch and TensorFlow in Python

Train a model on Visdrone-DET dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on Visdrone-DET dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about Visdrone-DET Dataset

Visdrone-DET Dataset Description

Homepage: http://aiskyeye.com/download/
Repository: https://github.com/VisDrone/VisDrone-Dataset
Paper: Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin: Detection and Tracking Meet Drones Challenge

Visdrone-DET Dataset Curators

Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin

Visdrone-DET Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

Visdrone-DET Dataset Citation Information

				
					@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}}