Introduction #
The Semantic Segmentation Makassar (IDN) Road Dataset is a valuable resource for computer vision and autonomous vehicle development. It comprises 374 annotated images captured in Makassar City, South Sulawesi, using a dashcam with 2560 x 1600-pixel resolution. These images are manually labeled into three classes: road, lm_solid (Lane mark solid), and lm_dashed (Lane mark dashed).
This dataset serves as a versatile resource for training and testing semantic segmentation models, facilitating advancements in autonomous vehicles and computer vision applications. Explore the Makassar Road Dataset to drive innovation in intelligent transportation systems and related fields.
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HomepageSummary #
Semantic Segmentation Makassar (IDN) Road is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the automotive industry.
The dataset consists of 374 images with 872 labeled objects belonging to 3 different classes including road, lm_solid, and lm_dashed.
Images in the Makassar Road dataset have pixel-level semantic segmentation annotations. 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 2022.
Explore #
Makassar Road dataset has 374 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.
Class balance #
There are 3 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 |
|---|---|---|---|---|
roadâž” mask | 374 | 374 | 1 | 17.85% |
lm_solidâž” mask | 299 | 299 | 1 | 0.61% |
lm_dashedâž” mask | 199 | 199 | 1 | 0.16% |
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.
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 mask | 374 | 17.85% | 23.75% | 7.63% | 464px | 29% | 636px | 39.75% | 560px | 34.99% | 1029px | 40.2% | 2560px | 100% |
lm_solid mask | 299 | 0.61% | 1.5% | 0.05% | 80px | 5% | 582px | 36.38% | 490px | 30.6% | 307px | 11.99% | 2560px | 100% |
lm_dashed mask | 199 | 0.16% | 0.52% | 0% | 23px | 1.44% | 550px | 34.38% | 311px | 19.47% | 43px | 1.68% | 1421px | 55.51% |
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 872 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” | lm_solid mask | 0060_Image31.jpg | 1600 x 2560 | 187px | 11.69% | 1545px | 60.35% | 0.16% |
2âž” | road mask | 0060_Image31.jpg | 1600 x 2560 | 547px | 34.19% | 2417px | 94.41% | 18.02% |
3âž” | lm_dashed mask | 0060_Image31.jpg | 1600 x 2560 | 302px | 18.88% | 929px | 36.29% | 0.35% |
4âž” | lm_solid mask | 20220311144930_0060 139.jpg | 1600 x 2560 | 513px | 32.06% | 1992px | 77.81% | 0.86% |
5âž” | road mask | 20220311144930_0060 139.jpg | 1600 x 2560 | 557px | 34.81% | 2560px | 100% | 18.73% |
6âž” | lm_solid mask | 20220311151533_0060 001.jpg | 1600 x 2560 | 552px | 34.5% | 2154px | 84.14% | 0.68% |
7âž” | road mask | 20220311151533_0060 001.jpg | 1600 x 2560 | 599px | 37.44% | 2337px | 91.29% | 20.46% |
8âž” | lm_dashed mask | 20220311151533_0060 001.jpg | 1600 x 2560 | 515px | 32.19% | 755px | 29.49% | 0.21% |
9âž” | lm_solid mask | 20220311151833_0060 009.jpg | 1600 x 2560 | 504px | 31.5% | 2193px | 85.66% | 0.65% |
10âž” | road mask | 20220311151833_0060 009.jpg | 1600 x 2560 | 548px | 34.25% | 2437px | 95.2% | 21.44% |
License #
Citation #
If you make use of the Makassar Road data, please cite the following reference:
@dataset{Makassar Road,
author={Nublan Azqalani},
title={Semantic Segmentation Makassar(IDN) Road},
year={2022},
url={https://www.kaggle.com/datasets/nublanazqalani/semantic-segmentation-makassaridn-road-dataset}
}
If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:
@misc{ visualization-tools-for-makassar-road-dataset,
title = { Visualization Tools for Makassar Road Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/makassar-road } },
url = { https://datasetninja.com/makassar-road },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { nov },
note = { visited on 2024-11-21 },
}Download #
Dataset Makassar Road 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='Makassar Road', 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.
Disclaimer #
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