Introduction #
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.
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.
![Dataset Poster](https://cdn.datasetninja.com/github/dataset-ninja/carvana-image-masking/main/visualizations/poster.png?width=1200)
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 image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/g/t/4L/WgTu29UqCqmyIU2hojgUQpfgWW6jEAunCU4nbF3edKOdHcfyo5jHvwZWrmYSS2LF249AfKduR09cQIytf48wgeS56O4dG4D6i0LF4bIGIynq7fgsogUOYD47pMnp.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/1/2/1w/NU1dc6DcBGvzIm078nmW6qIN9KKCIk6eYpgP9hGyTkdO3iPfexuYfW97NFSc7t3dRiR6FHpfnvLTLRALgNoZUOxXS8UQvo6oRyhD9P4ugBebhZVInSgtHPUVRTY0.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/V/I/e8/x5T8WZhJsAtxJ34nRq6CGUf7ktmWyL6itAkZXiy0Owa10UuDZrrv8Th8Pb5SBTw5uNYJhkG4TjedRg9QCeYQQ2da53TRg8aMxCB7KyKmSkPGO3cyraYMsmDcDkTv.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/i/4/C6/uAa7nJ0X73AlPEAMA3NgAZAiwzauB6yidyqiOCcZ5GKShfsQILzS9v56KMR0cSUGbsJwMbWgCD8yrfKUOskYtgcNnJAvjIeAmpd8D8VBaNjyPxnIuJKlw74qbTlL.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/e/l/iK/AfrLbIlWeu27FmhpBdUDU4kCheZaNpOEnW1S9l0PP1KgUZRYVm58zRvHFFQHxy7m7N8JQTiI2MNFEkRgC0qtvU0TbpJj8FQDYxYwFIVFAxPSM4OLgCaSV9pYrcux.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/6/o/Zg/1gPdf8u1RbXUPHYUsFYiQsreSt0ipdmoe3Jdxg8191QpxvMw4Iii3rQy2ySBHpOBlIM0NHsA0wsXJbsRkPsRrw2vSNoK4eq2GFgvqy9SiTavf6YHNkJ6wjtltmbh.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/F/I/l1/EGJXJYpeZz6srVIcaFyEurkO9dAk1DpgMzWASlMj5fcnWtPde2uom2S28BFlvkv1esBkwbQgDRDVSPuTuWaEc0qJZTXCbnCRbVuERmXUbYsQntwehjb98O4XDj6r.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/A/b/tl/erGkhzNT40EaRuoor6DJCq6eoB6Qs1IsSrCC80GidpM91TXBJA4vnghEt6QlFB6NrfOBtNmXljzgJCa7EslZ4ct6OJTwKGHoepDr64KDGPxzhI1ZSR5yXfSfDIe2.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/b/C/HG/fYrIG0cpxdX8LUElXGPwkPwTGsAzRR1qQOtrxnVeMJTvRRuiCeBgTp27ObaaaxeORlZAyGem0wdPNLjsJxKLsI7p0U6pV6iMJELcBX8Nek51J1SeG4RHF0G5Wj0K.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/Y/8/oJ/f1XwdYvkGzyIXUcWoXZ74gIqPmwolZQEex8sBdoDC8GzoWvClxHsJKbk6WZJumdPRz5tiUd9OgGoucMdt9f0BB2CDZeYcQDHmFOLuER7bv7a1DypmkYbLzlD5WZo.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/L/p/M4/JysMXSfpGiByqEwmlmmWWC029EmCtkb7ivA2cVtMsXW8yNs3sM6gWLFscC2sQFLHjjtI2tV8yyfTvFNAhot2Mi6pkd0m5dOw3jCDMPq9PZnAXCdKux5vIaJm8m0w.jpg)
![Sample image from Carvana Image Masking 2017](https://cdn.datasetninja.com/previews/q/ext:jpeg/resize:fill:400:0:0/q:70/plain/supervisely-supervisely-assets-public/images/original/1/a/tw/wAplCwbbZ8T596sWcHcLarPmw8L4IMUyjCpXTttw4UYehyg5CK3j2tmmfrRF9bK9bJlzirxMXGR1oSCHuYGLAPyY28V9mBBgqBinJmbJL2JLj2lTkAQlTZ20QoqU.jpg)
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.
Class ã…¤ | Images ã…¤ | Objects ã…¤ | Count on image average | Area on image average |
---|---|---|---|---|
carâž” 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.
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.
![Spatial Heatmap](https://cdn.datasetninja.com/github/dataset-ninja/carvana-image-masking/main/stats/classes_heatmaps.png)
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.
Object ID ã…¤ | Class ã…¤ | Image name click row to open | Image size height x width | Height ã…¤ | Height ã…¤ | Width ã…¤ | Width ã…¤ | Area ã…¤ |
---|---|---|---|---|---|---|---|---|
1âž” | car mask | 93881caf9f70_16.jpg | 1280 x 1918 | 716px | 55.94% | 1071px | 55.84% | 21.52% |
2âž” | car mask | 34defeec3ce6_16.jpg | 1280 x 1918 | 707px | 55.23% | 1083px | 56.47% | 21.97% |
3âž” | car mask | 3f8d611822bc_09.jpg | 1280 x 1918 | 483px | 37.73% | 675px | 35.19% | 11.27% |
4âž” | car mask | d22e6a2ca6df_11.jpg | 1280 x 1918 | 763px | 59.61% | 1396px | 72.78% | 31.41% |
5âž” | car mask | 26ccab021981_04.jpg | 1280 x 1918 | 710px | 55.47% | 1548px | 80.71% | 28.65% |
6âž” | car mask | 79a7691a90b1_08.jpg | 1280 x 1918 | 518px | 40.47% | 845px | 44.06% | 13.18% |
7âž” | car mask | fdc2c87853ce_06.jpg | 1280 x 1918 | 678px | 52.97% | 1478px | 77.06% | 28.15% |
8âž” | car mask | 64f701f36437_15.jpg | 1280 x 1918 | 614px | 47.97% | 1137px | 59.28% | 18.75% |
9âž” | car mask | 3f3e362dea23_16.jpg | 1280 x 1918 | 738px | 57.66% | 912px | 47.55% | 19.95% |
10âž” | 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.
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
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 = { sep },
note = { visited on 2024-09-08 },
}
Download #
Please visit dataset homepage to download the data.
Disclaimer #
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