Dataset Ninja LogoDataset Ninja:

ABU Robocon 2021 Pot Dataset

187411515
Tagrobotics
Taskobject detection
Release YearMade in 2022
LicenseDbCL v1.0
Download3 GB

Introduction #

Wing-Fung Ku

The ABU Robocon 2021 Pot is a collection of labeled RGB images, expertly organized in the YOLOv4 style, providing a unique and valuable resource for researchers and enthusiasts in the field of robotics and computer vision. This dataset comprises 1552 images in labeled split, with 1304 images meticulously marked to precisely identify the pot class, and an additional 322 images in the negativeimages_raw split, which may have been utilized for further experimentation and training. This comprehensive dataset is poised to empower future Robocon contestants and researchers, equipping them with the tools needed to tackle the distinctive challenges presented by the ABU Robocon Pot in the context of object detection and robotic competition.

file prefix explanation:

D455_ : the RGB image is obtained from realsence_d455 depth camera.
K4A_: the RGB image is obtained from azure_kinect depth camera.
L515_: the RGB image is obtained from realsence_l515_lidar.

Rb2017, Rb2018, Rb2019, Rb2020, Rb2021: the RGB image obtained by arbitrary_device with blank label. Basically used to reduce false positives of detection.

You could instead use the images in the folder NegativeImages_raw as negative samples for any purpose to boost your model detection for Robocon. This folder contains random photos from previous ABU Robocon in Hong Kong Science Park.

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Dataset LinkHomepage

Summary #

ABU Robocon 2021 Pot is a dataset for an object detection task. It is used in the robotics industry.

The dataset consists of 1874 images with 7495 labeled objects belonging to 1 single class (pot).

Images in the ABU Robocon 2021 Pot dataset have bounding box annotations. There are 570 (30% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: labeled (1552 images) and negativeimages_raw (322 images). Also, the dataset contains camera tag, negativeimages_raw split also contains year tag. The dataset was released in 2022 by the PolyU FENG Robotics Team.

Dataset Poster

Explore #

ABU Robocon 2021 Pot dataset has 1874 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 ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
OpenSample annotation mask from ABU Robocon 2021 PotSample image from ABU Robocon 2021 Pot
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Have a look at 1874 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.

Search
Rows 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
potβž”
rectangle
1304
7495
5.75
4.83%

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 1-1 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
pot
rectangle
7495
0.85%
7.71%
0.09%
28px
5.56%
316px
43.89%
87px
13.01%
19px
1.64%
225px
17.58%

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 7495 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 7495
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”
pot
rectangle
D455_70.jpg
480 x 640
64px
13.33%
47px
7.34%
0.98%
2βž”
pot
rectangle
D455_70.jpg
480 x 640
67px
13.96%
46px
7.19%
1%
3βž”
pot
rectangle
D455_70.jpg
480 x 640
45px
9.38%
31px
4.84%
0.45%
4βž”
pot
rectangle
D455_70.jpg
480 x 640
40px
8.33%
26px
4.06%
0.34%
5βž”
pot
rectangle
D455_70.jpg
480 x 640
30px
6.25%
21px
3.28%
0.21%
6βž”
pot
rectangle
D455_70.jpg
480 x 640
50px
10.42%
35px
5.47%
0.57%
7βž”
pot
rectangle
D455_67_11_540.jpg
720 x 1280
93px
12.92%
83px
6.48%
0.84%
8βž”
pot
rectangle
D455_67_11_540.jpg
720 x 1280
185px
25.69%
148px
11.56%
2.97%
9βž”
pot
rectangle
D455_67_11_540.jpg
720 x 1280
79px
10.97%
51px
3.98%
0.44%
10βž”
pot
rectangle
D455_67_11_540.jpg
720 x 1280
179px
24.86%
121px
9.45%
2.35%

License #

ABU Robocon 2021 Pot is under DbCL v1.0 license.

Source

Citation #

If you make use of the ABU Robocon 2021 Pot data, please cite the following reference:

@misc{Ku_KaggleABURobocon2021,
  author={Wing-Fung Ku},
  title={ABU Robocon 2021 Pot Dataset, vinesmsuic},
  month={Jul},
  year={2021},
  howpublished={\url{https://www.kaggle.com/datasets/vinesmsuic/abu-robocon-2021-pot-dataset}},
}

Source

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

@misc{ visualization-tools-for-abu-robocon-2021-pot-dataset,
  title = { Visualization Tools for ABU Robocon 2021 Pot Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/abu-robocon-2021-pot } },
  url = { https://datasetninja.com/abu-robocon-2021-pot },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset ABU Robocon 2021 Pot 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='ABU Robocon 2021 Pot', 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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