---
license: mit
task_categories:
- image-classification
tags:
- pcb
- component-classification
- electronics
- computer-vision
- random-forest
pretty_name: PCB Component Classifier Dataset
size_categories:
- 1K<n<10K
---

# PCB Component Classifier Dataset

1,120 individually photographed electronic components (resistors, capacitors, diodes, inductors, transistors, ICs), used to train an explainable Random-Forest classifier over 33 hand-engineered CV features.

Part of the [PCB Inspection Suite](https://huggingface.co/ParsaVictor/pcb-inspection-suite). Pipeline, training code, and results: [GitHub: pcb-component-classifier](https://github.com/ParsaVictor/pcb-component-classifier).

## Classes and splits

| Split | Images |
|---|---|
| train | 1,000 |
| holdout | 120 (never used in training or tuning) |

| Class | Description |
|---|---|
| Resistors | |
| Capacitors | |
| Diodes | |
| Inductors | |
| Transistors | |
| ICs | |

## Format

Flat folders of `<class>_<n>.png`, one component per image, packaged as two archives:

- `train.zip`
- `holdout.zip`

## Quick start

```python
from huggingface_hub import hf_hub_download
import zipfile

for split in ["train", "holdout"]:
    zip_path = hf_hub_download(
        repo_id="ParsaVictor/pcb-component-classifier",
        repo_type="dataset",
        filename=f"{split}.zip",
    )
    with zipfile.ZipFile(zip_path) as zf:
        zf.extractall(split)
```

## Related

- Trained model: [pcb-component-classifier](https://huggingface.co/ParsaVictor/pcb-component-classifier) — 93.3% holdout accuracy, decision-path explanations
- Training code: [GitHub: pcb-component-classifier](https://github.com/ParsaVictor/pcb-component-classifier)
- Full suite overview: [pcb-inspection-suite](https://huggingface.co/ParsaVictor/pcb-inspection-suite)

## License

MIT.