PCB Component Detection - Dataset Ninja

Summary #

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.

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.

Sample annotation mask from PCB Component Detection

Sample image from PCB Component Detection

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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.

Rows 1-9 of 9

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
Transformer➔ Unknown	1295	2547	1.97	9.81%
Cap1➔ Unknown	1295	1295	1	2.9%
MOSFET➔ rectangle	1293	1295	1	1.8%
Mov➔ Unknown	1291	1291	1	2.81%
Cap2➔ Unknown	1264	1264	1	1.32%
Cap3➔ Unknown	1199	1199	1	2.99%
Resistor➔ Unknown	1145	1151	1.01	0.81%
Cap4➔ Unknown	1062	1074	1.01	3.02%
Resestor➔ 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.

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.

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.

Rows 1-10 of 11119

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➔	Resistor Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	76px	5.73%	72px	7.24%	0.42%
2➔	Transformer Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	197px	14.86%	134px	13.48%	2%
3➔	Cap4 Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	159px	11.99%	121px	12.17%	1.46%
4➔	Cap3 Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	149px	11.24%	125px	12.58%	1.41%
5➔	Cap2 Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	81px	6.11%	80px	8.05%	0.49%
6➔	Cap1 Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	137px	10.33%	119px	11.97%	1.24%
7➔	Transformer Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	166px	12.52%	205px	20.62%	2.58%
8➔	Mov Unknown	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	92px	6.94%	165px	16.6%	1.15%
9➔	MOSFET rectangle	VID202106071443181-24_jpg.rf.9c0b6aabe402434a8b4a074eef208ef5.jpg	1326 x 994	95px	7.16%	109px	10.97%	0.79%
10➔	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 = { dec },
  note = { visited on 2024-12-03 },
}

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.

. . .

Disclaimer #

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