README.md

---
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- image-classification
task_ids: []
pretty_name: visdrone-mot
tags:
- fiftyone
- image
- image-classification
dataset_summary: '




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2846 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include ''max_samples'', etc

  dataset = load_from_hub("voxel51/visdrone-mot")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for VisDrone2019-DET


![image/png](visdrone-explore.gif)


This is a [FiftyOne](https://github.com/voxel51/fiftyone) version of the VisDrone2019-DET 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
# 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)
```

To render the sequential frames as video in the FiftyOne app you can use [`group_by()`](https://beta-docs.voxel51.com/fiftyone_concepts/using_views/#grouping) to create a view that groups the data by scene, ordered by frame number/timestamp. When you load a [dynamic](https://beta-docs.voxel51.com/fiftyone_concepts/using_datasets/#dynamic-attributes) grouped view in the App, you'll have the same experience as video datasets:

```python
from fiftyone import ViewField as F

view = dataset.group_by(
    "scene_id",
    order_by="frame_number"
)

# Save the view for easy loading in the App 
dataset.save_view("scenes", view)
```

See the gif above for details on how to render this view as a video in the app.

## Dataset Details

### Dataset Description

This dataset is the validation split of the VisDrone-MOT dataset.

- **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
- **Shared by:** [Harpreet Sahota](https://huggingface.co/harpreetsahota, Hacker-in-Residence at Voxel51

### Dataset Sources
- **Repository:** https://github.com/VisDrone/VisDrone-Dataset
- **Paper:** [Detection and Tracking Meet Drones Challenge](https://arxiv.org/abs/2001.06303)



## Dataset Structure

```plaintext
Name:        visdrone-mot
Media type:  image
Num samples: 2846
Persistent:  True
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)
    created_at:         fiftyone.core.fields.DateTimeField
    last_modified_at:   fiftyone.core.fields.DateTimeField
    scene_id:           fiftyone.core.fields.StringField
    language:           fiftyone.core.fields.StringField
    frame_number:       fiftyone.core.fields.IntField
    scene_type:         fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    time_of_day:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    pedestrian_density: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    detections:         fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
```



## Dataset Creation

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

### Synthetically Generated Fields

This dataset has synthetically generated fields which were generated by a human annotator (Harpreet Sahota). Below is a description of the generated fields:

```python
scene_attributes = {
    "uav0000086_00000_v": {
        "scene_type": "sporting event",
        "time_of_day": "daytime",
        "pedestrian_density": "high"
    },
    "uav0000117_02622_v": {
        "scene_type": "intersection",
        "time_of_day": "night",
        "pedestrian_density": "medium"
    },
    "uav0000137_00458_v": {
        "scene_type": "intersection",
        "time_of_day": "daytime",
        "pedestrian_density": "high"
    },
    "uav0000182_00000_v": {
        "scene_type": "road",
        "time_of_day": "daytime",
        "pedestrian_density": "low"
    },
    "uav0000268_05773_v": {
        "scene_type": "road",
        "time_of_day": "daytime",
        "pedestrian_density": "low"
    },
    "uav0000305_00000_v": {
        "scene_type": "intersection",
        "time_of_day": "daytime",
        "pedestrian_density": "low"
    },
    "uav0000339_00001_v": {
        "scene_type": "intersection",
        "time_of_day": "dusk",
        "pedestrian_density": "low"
    }
}

scene_language = {
    "uav0000086_00000_v": "A drone flies over a large crowd of people at a sporting complex where people are playing basketball.",
    "uav0000117_02622_v": "This scene shows a busy intersection at night with cars and pedestrians moving around. There seems to be a festial going on.",
    "uav0000137_00458_v": "This scene is a chaotic intersection with cars and pedestrians moving around. No one seems to be following the traffic rules.",
    "uav0000182_00000_v": "This scene shows a drone flying over a road with cars moving in both directions. The road is surrounded by trees.",
    "uav0000268_05773_v": "This scene depicts a highway with cars moving in both directions. The highway is surrounded by trees and buildings.",
    "uav0000305_00000_v": "This scene is a direct overhead shot of an intersection with cars and pedestrians moving around. Traffic seems to be orderly.",
    "uav0000339_00001_v": "This scene is a drone shot of an intersection at dusk with cars, motorcycles, and pedestrians moving around. The scene is well lit."
}
```

### 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]([email protected]) 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:**

```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](https://github.com/VisDrone/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.