MithatGuner/resistordataset

Dataset Labels

['resistor']

Number of Images

{'valid': 6, 'test': 3, 'train': 126}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("MithatGuner/resistordataset", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/harish-madhavan/resistordataset/dataset/1

Citation

@misc{
                            resistordataset_dataset,
                            title = { ResistorDataset Dataset },
                            type = { Open Source Dataset },
                            author = { Harish Madhavan },
                            howpublished = { \\url{ https://universe.roboflow.com/harish-madhavan/resistordataset } },
                            url = { https://universe.roboflow.com/harish-madhavan/resistordataset },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { sep },
                            note = { visited on 2024-07-16 },
                            }

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on December 7, 2022 at 8:42 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 unstructured image data
• annotate, and create datasets
• export, train, and deploy computer vision models
• use active learning to improve your dataset over time

It includes 135 images. Resistor are annotated in COCO format.

The following pre-processing was applied to each image:

• Auto-orientation of pixel data (with EXIF-orientation stripping)
• Resize to 416x416 (Stretch)
• Auto-contrast via adaptive equalization

The following augmentation was applied to create 3 versions of each source image:

• 50% probability of horizontal flip
• 50% probability of vertical flip

The following transformations were applied to the bounding boxes of each 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
• Randomly crop between 0 and 20 percent of the bounding box
• Random brigthness adjustment of between -25 and +25 percent
• Salt and pepper noise was applied to 5 percent of pixels