task_categories:
- object-detection
tags:
- pcb
- electronics
- yolo
- component-detection
pretty_name: PCB Components Labelled
PCB Components Labelled
YOLO-format object detection datasets and trained weights for detecting electronic components on PCB (printed circuit board) images. Two dataset/model pairs are included, covering a coarse 5-class label set and a fine-grained 17-class label set.
Contents
pcb_final_v4/ 17-class dataset (train/valid, YOLO format)
pcb_5class_tiled/ 5-class dataset, tiled images (train/valid, YOLO format)
models/17_class_pcb_v5_safe/ best.pt trained on pcb_final_v4
models/5_class_pcb_5class_v1/ best.pt trained on pcb_5class_tiled
Dataset: pcb_final_v4 (17 classes)
data.yaml:
nc: 17
names: ['IC', 'capacitor', 'connector', 'crystal_oscillator', 'diode', 'ferrite_bead',
'fuse', 'inductor', 'led', 'resistor', 'switch', 'transistor', 'button', 'em',
'pins', 'pads', 'test_point']
Dataset: pcb_5class_tiled (5 classes)
data.yaml:
names:
0: resistor
1: capacitor
2: ic
3: led
4: diode
Images are tiled crops (higher resolution training strategy), which is why this
dataset has far more files than pcb_final_v4 despite covering fewer classes.
Note: this dataset is only partially uploaded (~40%). The full local dataset
has 27,274 train images / 13,637 train labels / 6,934 valid images / 3,467 valid
labels (~51k files, ~22.5 GB). train/images and train/labels both exceed the
Hugging Face Hub's 10,000-files-per-directory git limit, which blocked further
uploads once each hit its cap (currently ~9,989 and ~9,999 files committed
respectively). valid/images and valid/labels are under the limit but were not
yet fully uploaded either. The 5_class_pcb_5class_v1 model below was trained on
the complete local dataset, not this partial upload — the model's reported metrics
still apply in full.
Models
Both are Ultralytics YOLO checkpoints (best.pt), trained for 150/150 epochs.
| Model | Dataset | Classes | mAP50 | mAP50-95 | Precision | Recall |
|---|---|---|---|---|---|---|
models/17_class_pcb_v5_safe/best.pt |
pcb_final_v4 | 17 | 0.709 | 0.511 | 0.736 | 0.683 |
models/5_class_pcb_5class_v1/best.pt |
pcb_5class_tiled | 5 | 0.857 | 0.565 | 0.854 | 0.780 |
args.yaml and results.csv from the corresponding training run are included
alongside each best.pt for reproducibility.
Usage
from ultralytics import YOLO
model = YOLO("models/17_class_pcb_v5_safe/best.pt")
results = model.predict("your_pcb_image.jpg")
License
Not yet specified by the dataset owner.