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Motorcycle Night Ride Dataset

2006218
Tagsafety
Taskinstance segmentation
Release YearMade in 2023
LicenseCC BY 4.0
Download919 MB

Introduction #

Sadhli Roomy, Mominul Islam, Abu Bakar Siddik Nayemet al.

The Motorcycle Night Ride Dataset is a collection of 200 frames taken from open media available on YouTube to enable testing for object detection and/or mobility-centered AI solutions - especially on motorcycle helmets with computer vision or other inventive avenues. Developing a dataset can be challenging given the cost of collection and labeling. Acme AI recognizes this bottleneck and is therefore creating open datasets for AI researchers, enthusiasts, and start-ups - enabling them to test their ideas.

Source data was collected from open media - particularly a video of the WatermelonMotoHead channel on YouTube. This particular dataset was a night scene named Harley Davidson 48 Night Ride which is based on a cruise in Tucson, Arizona, USA. The dataset is captured during night-time and therefore is subject to artifacts. The authors have used SuperAnnotate’s pixel editor as the tool for the semantics segmentation. It works on a raster logic as opposed to a vector one. Exporting includes the COCO format. They have prepackaged the dataset inclusive of fused images.

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Dataset LinkHomepage

Summary #

Motorcycle Night Ride is a dataset for instance segmentation and object detection tasks. It is used in the automotive industry.

The dataset consists of 200 images with 10697 labeled objects belonging to 6 different classes including rider, my bike, road, and other: undrivable, lane mark, and moveable.

Images in the Motorcycle Night Ride dataset have pixel-level instance segmentation and bounding box annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation task (only one mask for every class). All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2023 by the ACME Technologies Ltd., Bangladesh.

Here are the visualized examples for the classes:

Explore #

Motorcycle Night Ride dataset has 200 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
OpenSample annotation mask from Motorcycle Night RideSample image from Motorcycle Night Ride
👀
Have a look at 200 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-6 of 6
Class
Images
Objects
Count on image
average
Area on image
average
undrivable
any
200
529
2.65
70.58%
road
any
200
3623
18.11
63.91%
rider
any
200
628
3.14
35.2%
my bike
any
200
437
2.19
30.1%
lane mark
any
197
2719
13.8
35.4%
moveable
any
190
2761
14.53
7.37%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-6 of 6
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
road
any
3623
5.02%
93.61%
0%
1px
0.09%
1011px
93.61%
135px
12.5%
1px
0.05%
1920px
100%
moveable
any
2761
0.88%
34.07%
0%
2px
0.19%
915px
84.72%
81px
7.48%
1px
0.05%
1464px
76.25%
lane mark
any
2719
2.67%
81.02%
0%
1px
0.09%
875px
81.02%
89px
8.2%
1px
0.05%
1920px
100%
rider
any
628
13.76%
79.91%
0%
2px
0.19%
863px
79.91%
319px
29.53%
2px
0.1%
1920px
100%
undrivable
any
529
42.88%
100%
0%
1px
0.09%
1080px
100%
561px
51.94%
1px
0.05%
1920px
100%
my bike
any
437
21.26%
64.81%
0%
1px
0.09%
942px
87.22%
465px
43.08%
2px
0.1%
1920px
100%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 10697 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 10697
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
undrivable
any
Screenshot (357).png
1080 x 1920
255px
23.61%
439px
22.86%
2.42%
2
undrivable
any
Screenshot (357).png
1080 x 1920
410px
37.96%
1920px
100%
31.25%
3
undrivable
any
Screenshot (357).png
1080 x 1920
715px
66.2%
1920px
100%
66.2%
4
road
any
Screenshot (357).png
1080 x 1920
32px
2.96%
56px
2.92%
0.05%
5
road
any
Screenshot (357).png
1080 x 1920
8px
0.74%
15px
0.78%
0%
6
road
any
Screenshot (357).png
1080 x 1920
74px
6.85%
762px
39.69%
0.95%
7
road
any
Screenshot (357).png
1080 x 1920
17px
1.57%
37px
1.93%
0.01%
8
road
any
Screenshot (357).png
1080 x 1920
92px
8.52%
572px
29.79%
1.64%
9
road
any
Screenshot (357).png
1080 x 1920
35px
3.24%
53px
2.76%
0.04%
10
road
any
Screenshot (357).png
1080 x 1920
37px
3.43%
84px
4.38%
0.1%

License #

Motorcycle Night Ride is under CC BY 4.0 license.

Source

Citation #

If you make use of the Motorcycle Night Ride data, please cite the following reference:

 @misc{acme ai ltd._sadhli roomy_md. mominul islam_abu bakar siddik nayem_ashik mostofa tonmoy_sikder md. saiful islam_2022,
	 title={Motorcycle Night Ride (Semantic Segmentation)},
	 url={https://www.kaggle.com/dsv/4208825},
	 DOI={10.34740/KAGGLE/DSV/4208825},
	 publisher={Kaggle},
	 author={Acme AI Ltd. and Sadhli Roomy and Md. Mominul Islam and Abu Bakar Siddik Nayem and Ashik Mostofa Tonmoy and Sikder Md. Saiful Islam},
	 year={2022}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-motorcycle-night-ride-dataset,
  title = { Visualization Tools for Motorcycle Night Ride Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/motorcycle-night-ride } },
  url = { https://datasetninja.com/motorcycle-night-ride },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Motorcycle Night Ride can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Motorcycle Night Ride', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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