tutitata/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.