2020 NMLO Competition Six

Introduction #

The 2020 NMLO Competition Six dataset is specifically designed for object detection tasks, featuring 1176 images with annotations centered around a singular class — car. As an integral segment of the NMLO contest, part 6, this dataset is geared towards training object detectors utilizing the YOLO model architecture to detect custom objects within a series of images.

The dataset primarily consists of images depicting cars on roads, captured from a side-view-mounted camera on a vehicle. The training data includes annotations exclusively focusing on the car class or background (nothing), with bounding boxes specified to precisely train models to identify and locate cars within the images, providing a targeted resource for object detection model development.

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Summary #

2020 NMLO Competition Six is a dataset for an object detection task. It is used in the surveillance industry.

The dataset consists of 1176 images with 559 labeled objects belonging to 1 single class (car).

Images in the 2020 NMLO Competition Six dataset have bounding box annotations. There are 821 (70% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (1001 images) and test (175 images). The dataset was released in 2020 by the Thomas Jefferson High School for Science and Technology Machine Learning Club, USA.

Explore #

2020 NMLO Competition Six dataset has 1176 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 2020 NMLO Competition Six

Sample image from 2020 NMLO Competition Six

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Have a look at 1176 images

Because of dataset's license preview is limited to 12 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 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 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
car➔ rectangle	355	559	1.57	3.15%

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 1-1 of 1

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
car rectangle	559	2.03%	15.53%	0.16%	18px	4.74%	138px	36.32%	46px	12.11%	20px	2.96%	300px	44.38%

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 559 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 559

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➔	car rectangle	vid_4_1760.jpg	380 x 676	46px	12.11%	113px	16.72%	2.02%
2➔	car rectangle	vid_4_1940.jpg	380 x 676	31px	8.16%	71px	10.5%	0.86%
3➔	car rectangle	vid_4_1940.jpg	380 x 676	41px	10.79%	57px	8.43%	0.91%
4➔	car rectangle	vid_4_12200.jpg	380 x 676	26px	6.84%	35px	5.18%	0.35%
5➔	car rectangle	vid_4_12200.jpg	380 x 676	54px	14.21%	46px	6.8%	0.97%
6➔	car rectangle	vid_4_6320.jpg	380 x 676	41px	10.79%	104px	15.38%	1.66%
7➔	car rectangle	vid_4_6320.jpg	380 x 676	42px	11.05%	91px	13.46%	1.49%
8➔	car rectangle	vid_4_1040.jpg	380 x 676	41px	10.79%	65px	9.62%	1.04%
9➔	car rectangle	vid_4_12080.jpg	380 x 676	39px	10.26%	57px	8.43%	0.87%
10➔	car rectangle	vid_4_16120.jpg	380 x 676	48px	12.63%	131px	19.38%	2.45%

License #

License is unknown for the 2020 NMLO Competition Six dataset.

Source

Citation #

If you make use of the 2020 NMLO Competition Six data, please cite the following reference:

@dataset{2020 NMLO Competition Six,
  organization={Thomas Jefferson High School for Science and Technology Machine Learning Club},
  title={2020 NMLO Competition Six},
  year={2020},
  url={https://tjmachinelearning.com/2020/comp_six}
}

Source

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

@misc{ visualization-tools-for-2020-nmlo-dataset,
  title = { Visualization Tools for 2020 NMLO Competition Six Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/2020-nmlo-6 } },
  url = { https://datasetninja.com/2020-nmlo-6 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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