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---
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](https://github.com/roboflow/roboflow-100-benchmark)
- **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
```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/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:
```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](https://universe.roboflow.com/roboflow-100/circuit-elements)
- Part of the [Roboflow 100 Benchmark](https://www.rf100.org/)
- Sponsored by Intel
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