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Hand-drawn Power Converter Circuit Diagrams
- Citation Author(s):
-
BharatBohara
University of HoustonHarish S.KrishnamoorthyUniversity of Houston - Submitted by:
- Bharat Bohara
- Last updated:
- Wed, 01/24/2024 - 17:26
- DOI:
- 10.21227/e2tg-zj02
- Data Format:
- License:
52 Views- Categories:
- Keywords:
Abstract
The dataset is created on June 8, 2023, at 12:28 AM, consists of 760 annotated images in Pascal VOC format. The dataset was exported via roboflow.com, an end-to-end computer vision platform that facilitates various tasks such as collaboration, image collection and organization, data understanding and search, annotation, dataset creation, model training and deployment, and active learning for dataset improvement. Each image in the dataset underwent pre-processing, which involved auto-orienting the pixel data by removing EXIF orientation, and resizing the images to a uniform size of 640x640 pixels through stretching. To increase the dataset's diversity and variability, the following augmentations were applied to generate five versions of each source image: horizontal flip (50% probability), vertical flip (50% probability), 90-degree rotations (none, clockwise, counter-clockwise, upside-down with equal probability), random rotation (-15 to +15 degrees), random shear (-15° to +15° horizontally and vertically), random brightness adjustment (-25% to +25%), random exposure adjustment (-25% to +25%), random Gaussian blur (0 to 1.25 pixels), and the application of salt and pepper noise to 3% of the pixels. Researchers and developers can utilize this dataset and access state-of-the-art computer vision training.
myHD-ECD - v1 2023-06-08 12:28am
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This dataset was exported via roboflow.com on June 8, 2023 at 5:30 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 760 images.
Components are annotated in Pascal VOC format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
The following augmentation was applied to create 5 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
* Random rotation of between -15 and +15 degrees
* Random shear of between -15° to +15° horizontally and -15° to +15° vertically
* Random brigthness adjustment of between -25 and +25 percent
* Random exposure adjustment of between -25 and +25 percent
* Random Gaussian blur of between 0 and 1.25 pixels
* Salt and pepper noise was applied to 3 percent of pixels
Documentation
| Attachment | Size |
|---|---|
 README.txt | 1.54 KB |
