You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
88 lines
5.2 KiB
88 lines
5.2 KiB
---
|
|
comments: true
|
|
description: Discover the VisDrone dataset, a comprehensive benchmark for drone-based computer vision tasks, including object detection, tracking, and crowd counting.
|
|
keywords: VisDrone Dataset, Ultralytics YOLO Docs, AISKYEYE, Lab of Machine Learning and Data Mining, Computer Vision tasks, drone-based image analysis, object detection, object tracking, crowd counting, YOLOv8n model
|
|
---
|
|
|
|
# VisDrone Dataset
|
|
|
|
The [VisDrone Dataset](https://github.com/VisDrone/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.
|
|
|
|
VisDrone is composed of 288 video clips with 261,908 frames and 10,209 static images, captured by various drone-mounted cameras. The dataset covers a wide range of aspects, including location (14 different cities across China), environment (urban and rural), objects (pedestrians, vehicles, bicycles, etc.), and density (sparse and crowded scenes). The dataset was collected using various drone platforms under different scenarios and weather and lighting conditions. These frames are manually annotated with over 2.6 million bounding boxes of targets such as pedestrians, cars, bicycles, and tricycles. Attributes like scene visibility, object class, and occlusion are also provided for better data utilization.
|
|
|
|
## Dataset Structure
|
|
|
|
The VisDrone dataset is organized into five main subsets, each focusing on a specific task:
|
|
|
|
1. **Task 1**: Object detection in images
|
|
2. **Task 2**: Object detection in videos
|
|
3. **Task 3**: Single-object tracking
|
|
4. **Task 4**: Multi-object tracking
|
|
5. **Task 5**: Crowd counting
|
|
|
|
## Applications
|
|
|
|
The VisDrone dataset is widely used for training and evaluating deep learning models in drone-based computer vision tasks such as object detection, object tracking, and crowd counting. The dataset's diverse set of sensor data, object annotations, and attributes make it a valuable resource for researchers and practitioners in the field of drone-based computer vision.
|
|
|
|
## Dataset YAML
|
|
|
|
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Visdrone dataset, the `VisDrone.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
|
|
|
|
!!! example "ultralytics/cfg/datasets/VisDrone.yaml"
|
|
|
|
```yaml
|
|
--8<-- "ultralytics/cfg/datasets/VisDrone.yaml"
|
|
```
|
|
|
|
## Usage
|
|
|
|
To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
|
|
|
|
!!! example "Train Example"
|
|
|
|
=== "Python"
|
|
|
|
```python
|
|
from ultralytics import YOLO
|
|
|
|
# Load a model
|
|
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
|
|
|
|
# Train the model
|
|
model.train(data='VisDrone.yaml', epochs=100, imgsz=640)
|
|
```
|
|
|
|
=== "CLI"
|
|
|
|
```bash
|
|
# Start training from a pretrained *.pt model
|
|
yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
|
|
```
|
|
|
|
## Sample Data and Annotations
|
|
|
|
The VisDrone dataset contains a diverse set of images and videos captured by drone-mounted cameras. Here are some examples of data from the dataset, along with their corresponding annotations:
|
|
|
|
|
|
|
|
- **Task 1**: Object detection in images - This image demonstrates an example of object detection in images, where objects are annotated with bounding boxes. The dataset provides a wide variety of images taken from different locations, environments, and densities to facilitate the development of models for this task.
|
|
|
|
The example showcases the variety and complexity of the data in the VisDrone dataset and highlights the importance of high-quality sensor data for drone-based computer vision tasks.
|
|
|
|
## Citations and Acknowledgments
|
|
|
|
If you use the VisDrone dataset in your research or development work, please cite the following paper:
|
|
|
|
```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}}
|
|
```
|
|
|
|
We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset). |