keremberke/pcb-defect-segmentation

Dataset Labels

['dry_joint', 'incorrect_installation', 'pcb_damage', 'short_circuit']

Number of Images

{'valid': 25, 'train': 128, 'test': 36}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("keremberke/pcb-defect-segmentation", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8

Citation

@misc{ defects-2q87r_dataset,
    title = { Defects Dataset },
    type = { Open Source Dataset },
    author = { Diplom },
    howpublished = { \\url{ https://universe.roboflow.com/diplom-qz7q6/defects-2q87r } },
    url = { https://universe.roboflow.com/diplom-qz7q6/defects-2q87r },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jan },
    note = { visited on 2023-01-27 },
}

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on January 27, 2023 at 1:45 PM 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 189 images. Defect are annotated in COCO format.

The following pre-processing was applied to each image:

No image augmentation techniques were applied.