license: other
license_name: research-use-mixed-sources
license_link: LICENSE.md
task_categories:
- object-detection
tags:
- pcb
- component-detection
- object-detection
- computer-vision
- yolo
- electronics
- printed-circuit-board
- pcb-components
- electronic-components
- manufacturing
- industrial-inspection
pretty_name: PCB Component Detection Dataset (High-Resolution)
size_categories:
- 1K<n<10K
PCB Component Detection Dataset (High-Resolution)
A YOLO-format object detection dataset for locating and classifying electronic components on a printed circuit board — resistors, capacitors, ICs, diodes, connectors, and more — from full high-resolution board photographs.
This dataset powers ParsaVictor/pcb-component-detection-yolo26, part of the PCB Inspection Suite.
Why high-resolution matters
A naive 640×640 export (the Roboflow default) shrinks small components like resistors down to a handful of pixels, which caps detector accuracy hard. This dataset was rebuilt from original full-resolution board photographs (median part size ~19px at the chosen tiling, vs ~6px in the original low-res export), with labels carefully re-mapped back onto the originals.
Classes (13)
| Class | # instances |
|---|---|
| Resistor | 34,470 |
| Capacitor | 33,923 |
| IC | 12,015 |
| Electrolytic Capacitor | 10,508 |
| Transistor | 4,049 |
| Diode | 3,904 |
| Inductor | 3,402 |
| Connector | 2,867 |
| Marker | 2,465 |
| Other | 1,680 |
| LED | 444 |
| Switch/Button | 287 |
| Oscillator | 268 |
Splits
| Split | Images |
|---|---|
| train | 3,005 |
| val | 321 |
| test | 292 |
Images are tiled crops from full-resolution board photographs (1024px tiles, 25% overlap) so components stay detectable at training resolution while the full board can still be reconstructed at inference time.
Format
Standard YOLO format:
images/{train,val,test}/*.jpg
labels/{train,val,test}/*.txt # class_id x_center y_center width height (normalized)
data.yaml
A simplified binary "any component" variant (single class, same images) is also used internally as a first detection stage before fine-grained classification; see the suite overview for the two-stage pipeline.
Quick start
from ultralytics import YOLO
model = YOLO("yolo26s.pt")
model.train(data="data.yaml", imgsz=1024, epochs=80)
from huggingface_hub import snapshot_download
path = snapshot_download(repo_id="ParsaVictor/pcb-component-detection", repo_type="dataset")
Related
- Sibling dataset: pcb-bare-board-defect-detection
- Sibling dataset: pcb-solder-defect-detection
- Full suite overview: pcb-inspection-suite
License / attribution
Released for research and development use. Built from manually re-annotated, full-resolution board photographs. Contact the author before any commercial redistribution. See LICENSE.md.