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LibreYOLO/printed-circuit-board

Printed Circuit Board This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains. Dataset Statistics Split Images Train 548 Validation 80 Test 44 Total 672 Classes (34) Battery Button Buzzer Capacitor Jumper Capacitor Clock Connector Diode Display EM Electrolytic Capacitor Ferrite Bead Fuse Heatsink IC Inductor Jumper Led PS Pads Pins Potentiometer… See the full description on the dataset page: https://huggingface.co/datasets/LibreYOLO/printed-circuit-board.

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Dataset Card

Printed Circuit Board

This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains.

Dataset Description

—Source: Roboflow 100
—Category: Real World
—License: CC-BY-4.0
—Format: YOLO (LibreYOLO compatible)
—Mirrored on: 2026-01-20

Dataset Statistics

Split	Images
Train	548
Validation	80
Test	44
Total	672

Classes (34)

—-
—Battery
—Button
—Buzzer
—Capacitor Jumper
—Capacitor
—Clock
—Connector
—Diode
—Display
—EM
—Electrolytic Capacitor
—Ferrite Bead
—Fuse
—Heatsink
—IC
—Inductor
—Jumper
—Led
—PS
—Pads
—Pins
—Potentiometer
—Resistor Jumper
—Resistor Network
—Resistor
—SK
—Switch
—Test Point
—Transformer
—Transistor
—Unknown Unlabeled
—Zener Diode
—iC

Usage

With LibreYOLO

python

from libreyolo import LIBREYOLO

# Load a model
model = LIBREYOLO(model_path="libreyoloXnano.pt")

# Train on this dataset
model.train(data='path/to/data.yaml', epochs=100)

Download from HuggingFace

python

from huggingface_hub import snapshot_download

# Download the dataset
snapshot_download(
    repo_id="Libre-YOLO/printed-circuit-board",
    repo_type="dataset",
    local_dir="./printed-circuit-board"
)

Directory Structure

printed-circuit-board/
├── data.yaml           # Dataset configuration
├── README.md           # This file
├── train/
│   ├── images/         # Training images
│   └── labels/         # Training labels (YOLO format)
├── valid/
│   ├── images/         # Validation images
│   └── labels/         # Validation labels
└── test/
    ├── images/         # Test images (if available)
    └── labels/         # Test labels

Label Format

Labels are in YOLO format (one .txt file per image):

<class_id> <x_center> <y_center> <width> <height>

All coordinates are normalized to [0, 1].

Citation

If you use this dataset, please cite the Roboflow 100 benchmark:

bibtex

@misc{rf100_2022,
    Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
    Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
    Year = {2022},
    Eprint = {arXiv:2211.13523},
}

License

This dataset is released under the CC-BY-4.0 license. Please check the original source for any additional terms.

Acknowledgments

—Original dataset from Roboflow Universe
—Part of the Roboflow 100 Benchmark
—Sponsored by Intel