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Indoor Objects Detection Dataset

1349101
Taggeneral
Taskobject detection
Release YearMade in 2022
LicenseGNU GPL 3.0
Download368 MB

Introduction #

Thepbordin Jaiinsom

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 β€œObject 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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Dataset LinkHomepageDataset LinkMediumDataset LinkGitHub

Summary #

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.

Dataset Poster

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.

OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
OpenSample annotation mask from Indoor Objects DetectionSample image from Indoor Objects Detection
πŸ‘€
Have a look at 1349 images
View images along with annotations and tags, search and filter by various parameters

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.

Search
Rows 1-10 of 10
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
cabinetDoorβž”
rectangle
589
4122
7
17.18%
refrigeratorDoorβž”
rectangle
418
883
2.11
24.4%
doorβž”
rectangle
397
610
1.54
21.11%
windowβž”
rectangle
275
557
2.03
7.08%
tableβž”
rectangle
236
315
1.33
15.22%
cabinetβž”
rectangle
210
263
1.25
30.16%
chairβž”
rectangle
167
340
2.04
9.14%
openedDoorβž”
rectangle
99
111
1.12
15.88%
couchβž”
rectangle
54
86
1.59
17.62%
poleβž”
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.

Search
Rows 1-10 of 10
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 7331
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
chair
rectangle
393.png
460 x 620
232px
50.43%
136px
21.94%
11.06%
2βž”
table
rectangle
393.png
460 x 620
297px
64.57%
269px
43.39%
28.01%
3βž”
refrigeratorDoor
rectangle
5c230f99fb6acb59.jpg
1024 x 1024
305px
29.79%
363px
35.45%
10.56%
4βž”
cabinetDoor
rectangle
5c230f99fb6acb59.jpg
1024 x 1024
372px
36.33%
237px
23.14%
8.41%
5βž”
refrigeratorDoor
rectangle
5c230f99fb6acb59.jpg
1024 x 1024
584px
57.03%
205px
20.02%
11.42%
6βž”
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%
7βž”
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%
8βž”
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%
9βž”
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%
10βž”
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/}
}

Source

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 = { jun },
  note = { visited on 2024-06-21 },
}

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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