Motorcycle Night Ride - Dataset Ninja

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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Homepage

Summary #

Motorcycle Night Ride is a dataset for instance segmentation, semantic 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.

Sample annotation mask from Motorcycle Night Ride

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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.

Rows 1-6 of 6

Class ㅤ	Images ㅤ	Objects ㅤ	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.

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.

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.

Rows 1-10 of 10697

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
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 = { nov },
  note = { visited on 2024-11-01 },
}

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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