Carvana Image Masking 2017

Introduction #

Brian Shaler, DanGill McDonald, Maggie McDonaldet al.

The Carvana Image Masking Dataset, presented as part of a computer vision challenge on the Kaggle platform, offered a unique opportunity for participants to tackle complex image segmentation tasks. Provided by Carvana, an online car retail company, this dataset consisted of high-resolution car images. The challenge tasked participants with the development of algorithms capable of automatically and precisely identifying cars within these images and generating pixel-level masks to outline the cars’ shapes.

This dataset contains a large number of car images (as .jpg files). Each car has exactly 16 images, each one taken at different angles. Each car has a unique id and images are named according to id_01.jpg, id_02.jpg … id_16.jpg. In addition to the images, you are also provided some basic metadata about the car make, model, year, and trim.

Note that some metadata values are missing.

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Homepage

Summary #

Carvana Image Masking 2017 is a dataset for a semantic segmentation task. It is used in the retail industry.

The dataset consists of 105152 images with 5088 labeled objects belonging to 1 single class (car).

Images in the Carvana Image Masking 2017 dataset have pixel-level semantic segmentation annotations. There are 100064 (95% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: test (100064 images) and train (5088 images). Also, the dataset contains car id, make, model, year, trim1, trim2 and angle_id tags. The dataset was released in 2017 by the Carvana.

Explore #

Carvana Image Masking 2017 dataset has 105152 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 Carvana Image Masking 2017

Sample image from Carvana Image Masking 2017

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Have a look at 105152 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➔ mask	5088	5088	1	21.07%

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 mask	5088	21.07%	41%	8.82%	428px	33.44%	1018px	79.53%	628px	49.08%	543px	28.31%	1918px	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 5088 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 5088

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 mask	93881caf9f70_16.jpg	1280 x 1918	716px	55.94%	1071px	55.84%	21.52%
2➔	car mask	34defeec3ce6_16.jpg	1280 x 1918	707px	55.23%	1083px	56.47%	21.97%
3➔	car mask	3f8d611822bc_09.jpg	1280 x 1918	483px	37.73%	675px	35.19%	11.27%
4➔	car mask	d22e6a2ca6df_11.jpg	1280 x 1918	763px	59.61%	1396px	72.78%	31.41%
5➔	car mask	26ccab021981_04.jpg	1280 x 1918	710px	55.47%	1548px	80.71%	28.65%
6➔	car mask	79a7691a90b1_08.jpg	1280 x 1918	518px	40.47%	845px	44.06%	13.18%
7➔	car mask	fdc2c87853ce_06.jpg	1280 x 1918	678px	52.97%	1478px	77.06%	28.15%
8➔	car mask	64f701f36437_15.jpg	1280 x 1918	614px	47.97%	1137px	59.28%	18.75%
9➔	car mask	3f3e362dea23_16.jpg	1280 x 1918	738px	57.66%	912px	47.55%	19.95%
10➔	car mask	2ea62c1beee7_04.jpg	1280 x 1918	522px	40.78%	1273px	66.37%	17.9%

License #

DATA

‘Data’ means the Data or Datasets linked from the Competition Website for the purpose of use by Participants in the Competition. For the avoidance of doubt, Data is deemed for the purpose of these Competition Rules to include any prototype or executable code provided to Participants by Kaggle or Competition Sponsor via the Website. Participants must use the Data only as permitted by these Competition Rules and any associated data use rules specified on the Competition Website.

Unless otherwise permitted by the terms of the Competition Website, Participants must use the Data solely for the purpose and duration of the Competition, including but not limited to reading and learning from the Data, analyzing the Data, modifying the Data and generally preparing your Submission and any underlying models and participating in forum discussions on the Website. Participants agree to use suitable measures to prevent persons who have not formally agreed to these Competition Rules from gaining access to the Data and agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Data to any party not participating in the Competition. Participants agree to notify Kaggle immediately upon learning of any possible unauthorized transmission or unauthorized access of the Data and agree to work with Kaggle to rectify any unauthorized transmission. Participants agree that participation in the Competition shall not be construed as having or being granted a license (expressly, by implication, estoppel, or otherwise) under, or any right of ownership in, any of the Data.

Source

Citation #

If you make use of the Carvana Image Masking 2017 data, please cite the following reference:

Brian Shaler, DanGill, Maggie, Mark McDonald, Patricia, Will Cukierski. (2017). Carvana 
Image Masking Challenge. Kaggle.
https://kaggle.com/competitions/carvana-image-masking-challenge

Source

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

@misc{ visualization-tools-for-carvana-image-masking-dataset,
  title = { Visualization Tools for Carvana Image Masking 2017 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/carvana-image-masking } },
  url = { https://datasetninja.com/carvana-image-masking },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { oct },
  note = { visited on 2024-10-31 },
}

Download #

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