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PCB Component Detection Dataset

141092029
Tagenvironmental
Taskobject detection
Release YearMade in 2021
LicenseCC0 1.0
Download180 MB

Summary #

Dataset LinkHomepage

Printed Board Circuit (PCB) Component Detection is a dataset for an object detection task. Possible applications of the dataset could be in the waste recycling industry.

The dataset consists of 1410 images with 11119 labeled objects belonging to 9 different classes including cap1, transformer, MOSFET, and other: mov, cap2, cap3, resistor, cap4, and resestor.

Images in the PCB Component Detection dataset have bounding box annotations. There are 114 (8% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (1099 images), validation (200 images), and test (111 images). The dataset was released in 2021.

Dataset Poster

Explore #

PCB Component Detection dataset has 1410 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 PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
OpenSample annotation mask from PCB Component DetectionSample image from PCB Component Detection
πŸ‘€
Have a look at 1410 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 9 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 1-9 of 9
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
Transformerβž”
Unknown
1295
2547
1.97
9.81%
Cap1βž”
Unknown
1295
1295
1
2.9%
MOSFETβž”
rectangle
1293
1295
1
1.8%
Movβž”
Unknown
1291
1291
1
2.81%
Cap2βž”
Unknown
1264
1264
1
1.32%
Cap3βž”
Unknown
1199
1199
1
2.99%
Resistorβž”
Unknown
1145
1151
1.01
0.81%
Cap4βž”
Unknown
1062
1074
1.01
3.02%
Resestorβž”
Unknown
3
3
1
1.03%

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 1-9 of 9
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
Transformer
Unknown
2547
4.99%
15.83%
0.54%
66px
5.88%
483px
41.25%
246px
19.69%
36px
4.23%
487px
55.09%
MOSFET
rectangle
1295
1.8%
4.63%
0%
2px
0.18%
261px
20.48%
143px
11.52%
2px
0.25%
265px
29.98%
Cap1
Unknown
1295
2.9%
6.9%
0.69%
63px
5.95%
288px
27.7%
182px
14.64%
77px
6.63%
291px
33.57%
Mov
Unknown
1291
2.81%
7.96%
0.05%
47px
3.61%
305px
26.22%
184px
14.71%
8px
1%
308px
35.31%
Cap2
Unknown
1264
1.32%
3.85%
0.16%
54px
4.61%
217px
22.1%
123px
9.87%
21px
2.59%
213px
25%
Cap3
Unknown
1199
2.99%
8.8%
0.42%
84px
5%
335px
31.91%
199px
15.94%
28px
3.48%
358px
34.1%
Resistor
Unknown
1151
0.8%
5.46%
0.06%
15px
1.43%
267px
19.8%
101px
8.05%
14px
1.76%
267px
32.09%
Cap4
Unknown
1074
2.99%
9.36%
0.33%
76px
6.27%
349px
34.11%
204px
16.45%
19px
2.2%
361px
38.11%
Resestor
Unknown
3
1.04%
1.2%
0.83%
106px
8.55%
124px
10.13%
115px
9.43%
98px
9.68%
118px
11.8%

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 11119 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 11119
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”
Resistor
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
76px
5.73%
72px
7.24%
0.42%
2βž”
Transformer
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
197px
14.86%
134px
13.48%
2%
3βž”
Cap4
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
159px
11.99%
121px
12.17%
1.46%
4βž”
Cap3
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
149px
11.24%
125px
12.58%
1.41%
5βž”
Cap2
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
81px
6.11%
80px
8.05%
0.49%
6βž”
Cap1
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
137px
10.33%
119px
11.97%
1.24%
7βž”
Transformer
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
166px
12.52%
205px
20.62%
2.58%
8βž”
Mov
Unknown
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
92px
6.94%
165px
16.6%
1.15%
9βž”
MOSFET
rectangle
VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg
1326 x 994
95px
7.16%
109px
10.97%
0.79%
10βž”
Resistor
Unknown
VID202106071439301-42_jpg.rf.05460987a3ac98466cfdef865421df67.jpg
1294 x 862
128px
9.89%
78px
9.05%
0.9%

License #

Printed Board Circuit (PCB) Component Detection is under CC0 1.0 license.

Source

Citation #

If you make use of the PCB Component Detection data, please cite the following reference:

@dataset{PCB Component Detection,
	author={Animeshkumar Nayak},
	title={Printed Board Circuit (PCB) Component Detection},
	year={2021},
	url={https://www.kaggle.com/datasets/animeshkumarnayak/pcb-fault-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-pcb-component-detection-dataset,
  title = { Visualization Tools for PCB Component Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/pcb-component-detection } },
  url = { https://datasetninja.com/pcb-component-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset PCB Component 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='PCB Component 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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