Datasets:

Libre-YOLO
/

circuit-elements

license: cc-by-4.0
task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow-100
  - rf100
  - yolo
  - libreyolo
  - real-world
  - computer-vision
  - bounding-box
pretty_name: Circuit Elements
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: string
  splits:
    - name: train
      num_examples: 672
    - name: validation
      num_examples: 64
    - name: test
      num_examples: 36

Circuit Elements

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	672
Validation	64
Test	36
Total	772

Classes (45)

Battery
Button
Buzzer
Capacitor Jumper
Capacitor Network
Capacitor
Clock
Conductor
Connector
Diode
Display
EM
Electrolytic Capacitor
Electrolytic capacitor
Ferrite Bead
Filter
Flex Cable
Fuse
Heatsink
IC
Indicator
Inductor
Jumper
Led
PS
Pads
Pins
Potentiometer
RC
RP
Resistance
Resistor Jumper
Resistor Network
Resistor
Resistort
Ressistor
SK
Switch
Test Point
Transducer
Transformer
Transistor
Unknown Unlabeled
Zener Diode
coil

Usage

With LibreYOLO

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

from huggingface_hub import snapshot_download

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

Directory Structure

circuit-elements/
├── 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:

@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