Introduction #
The creator of Indoor Objects Detection dataset was driven by the desire to assist individuals with visual impairments in their daily activities. This dataset forms an integral part of the “Object Detection for Blind People” project, which the author undertook during their involvement in the AI Builders 2022.
After some experimentation and refinement, the author defined the ultimate goal of their project: the detection of indoor objects. To achieve this, they took a sample from the Indoor Training Set (ITS) dataset, divided it into training, testing, and validation sets, performed image annotations, and trained a YOLOv5 model.
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GitHubSummary #
Indoor Objects Detection is a dataset for an object detection task. It is applicable or relevant across various domains.
The dataset consists of 1349 images with 7331 labeled objects belonging to 10 different classes including cabinetDoor, refrigeratorDoor, door, and other: window, table, cabinet, chair, openedDoor, couch, and pole.
Images in the Indoor Objects Detection dataset have bounding box annotations. There are 154 (11% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (1012 images), valid (230 images), and test (107 images). The dataset was released in 2022.
Explore #
Indoor Objects Detection dataset has 1349 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 10 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 ㅤ | Images ㅤ | Objects ㅤ | Count on image average | Area on image average |
|---|---|---|---|---|
cabinetDoor➔ rectangle | 589 | 4122 | 7 | 17.18% |
refrigeratorDoor➔ rectangle | 418 | 883 | 2.11 | 24.4% |
door➔ rectangle | 397 | 610 | 1.54 | 21.11% |
window➔ rectangle | 275 | 557 | 2.03 | 7.08% |
table➔ rectangle | 236 | 315 | 1.33 | 15.22% |
cabinet➔ rectangle | 210 | 263 | 1.25 | 30.16% |
chair➔ rectangle | 167 | 340 | 2.04 | 9.14% |
openedDoor➔ rectangle | 99 | 111 | 1.12 | 15.88% |
couch➔ rectangle | 54 | 86 | 1.59 | 17.62% |
pole➔ rectangle | 21 | 44 | 2.1 | 9.77% |
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cabinetDoor rectangle | 4122 | 2.51% | 57.48% | 0% | 1px | 0.13% | 889px | 98.53% | 125px | 16.17% | 1px | 0.1% | 1021px | 99.71% |
refrigeratorDoor rectangle | 883 | 11.59% | 98.44% | 0.07% | 27px | 3.22% | 982px | 98.44% | 380px | 45.47% | 13px | 1.27% | 1024px | 100% |
door rectangle | 610 | 13.75% | 99.07% | 0% | 1px | 0.15% | 3914px | 100% | 564px | 54.85% | 2px | 0.2% | 1838px | 100% |
window rectangle | 557 | 3.5% | 27.27% | 0.06% | 20px | 2.93% | 847px | 86.54% | 169px | 23.57% | 10px | 0.98% | 1066px | 55.06% |
chair rectangle | 340 | 4.52% | 69.06% | 0.09% | 12px | 2.34% | 730px | 95.05% | 163px | 24.88% | 14px | 1.37% | 793px | 77.44% |
table rectangle | 315 | 11.41% | 97.36% | 0.25% | 10px | 2.17% | 865px | 97.45% | 197px | 27.56% | 35px | 3.81% | 1932px | 99.9% |
cabinet rectangle | 263 | 24.09% | 95.01% | 0.17% | 20px | 4.35% | 972px | 99.48% | 372px | 47.11% | 18px | 1.76% | 1015px | 99.12% |
openedDoor rectangle | 111 | 14.21% | 78.22% | 0.9% | 103px | 13.84% | 3435px | 100% | 469px | 58.01% | 27px | 3.12% | 2563px | 90.25% |
couch rectangle | 86 | 11.17% | 84.73% | 1.02% | 56px | 8.11% | 742px | 96.61% | 165px | 30.53% | 50px | 4.88% | 898px | 94.19% |
pole rectangle | 44 | 4.66% | 17.83% | 0.74% | 57px | 11.86% | 832px | 99.61% | 404px | 52.44% | 22px | 3.03% | 241px | 38.87% |
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 7331 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 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➔ | chair rectangle | 393.png | 460 x 620 | 232px | 50.43% | 136px | 21.94% | 11.06% |
2➔ | table rectangle | 393.png | 460 x 620 | 297px | 64.57% | 269px | 43.39% | 28.01% |
3➔ | refrigeratorDoor rectangle | 5c230f99fb6acb59.jpg | 1024 x 1024 | 305px | 29.79% | 363px | 35.45% | 10.56% |
4➔ | cabinetDoor rectangle | 5c230f99fb6acb59.jpg | 1024 x 1024 | 372px | 36.33% | 237px | 23.14% | 8.41% |
5➔ | refrigeratorDoor rectangle | 5c230f99fb6acb59.jpg | 1024 x 1024 | 584px | 57.03% | 205px | 20.02% | 11.42% |
6➔ | cabinetDoor rectangle | kitchen-cabinet-handles-interesting-inspiration-door-simple-ideas-decor-gold-pulls-knobs-and-furniture-hardware-dresser-hoosier-recycling-bins-black-knob-with-backplate-stone-ring.jpg | 1024 x 1024 | 495px | 48.34% | 328px | 32.03% | 15.48% |
7➔ | cabinetDoor rectangle | kitchen-cabinet-handles-interesting-inspiration-door-simple-ideas-decor-gold-pulls-knobs-and-furniture-hardware-dresser-hoosier-recycling-bins-black-knob-with-backplate-stone-ring.jpg | 1024 x 1024 | 116px | 11.33% | 346px | 33.79% | 3.83% |
8➔ | cabinet rectangle | kitchen-cabinet-handles-interesting-inspiration-door-simple-ideas-decor-gold-pulls-knobs-and-furniture-hardware-dresser-hoosier-recycling-bins-black-knob-with-backplate-stone-ring.jpg | 1024 x 1024 | 763px | 74.51% | 1015px | 99.12% | 73.86% |
9➔ | cabinetDoor rectangle | kitchen-cabinet-handles-interesting-inspiration-door-simple-ideas-decor-gold-pulls-knobs-and-furniture-hardware-dresser-hoosier-recycling-bins-black-knob-with-backplate-stone-ring.jpg | 1024 x 1024 | 116px | 11.33% | 340px | 33.2% | 3.76% |
10➔ | cabinetDoor rectangle | kitchen-cabinet-handles-interesting-inspiration-door-simple-ideas-decor-gold-pulls-knobs-and-furniture-hardware-dresser-hoosier-recycling-bins-black-knob-with-backplate-stone-ring.jpg | 1024 x 1024 | 125px | 12.21% | 313px | 30.57% | 3.73% |
License #
Indoor Objects Detection is under GNU GPL 3.0 license.
Citation #
If you make use of the Indoor Objects Detection data, please cite the following reference:
@dataset{Indoor Objects Detection,
author={Thepbordin Jaiinsom},
title={Indoor Objects Detection},
year={2022},
url={https://www.kaggle.com/datasets/thepbordin/indoor-object-detection/}
}
If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:
@misc{ visualization-tools-for-indoor-object-detection-dataset,
title = { Visualization Tools for Indoor Objects Detection Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/indoor-object-detection } },
url = { https://datasetninja.com/indoor-object-detection },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { nov },
note = { visited on 2024-11-21 },
}Download #
Dataset Indoor Objects Detection 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='Indoor Objects Detection', 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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