---
license: mit
tags:
  - object-detection
  - pcb
  - yolo
  - rf-detr
  - computer-vision
  - aoi
  - pytorch
datasets:
  - custom
---

# PCBInspect-AI - Model Weights

Model weights for **PCBInspect-AI**, a deep learning platform for automated PCB (Printed Circuit Board) feature detection in Automated Optical Inspection (AOI) pipelines.

Source code and full documentation: [JC-prog/pcb-inspect-ai](https://github.com/JC-prog/pcb-inspect-ai)

---

## Models

### 1. YOLOv12-Medium (Fine-Tuned) - Recommended

**File:** `YoloV12-Medium-160-FineTuned.pt`

Two-stage fine-tuned YOLOv12m for PCB feature detection. Trained over 160 epochs (100 pretraining + 60 fine-tuning).

| Metric | Score |
|---|---|
| mAP@0.5 | 0.839 |
| mAP@0.5:0.95 | 0.741 |
| Precision | 0.974 |
| Recall | 0.779 |
| Epochs | 160 (100 + 60) |

**Recommended for deployment** — highest recall minimises missed defects, critical for AOI.

### 2. RF-DETR Medium (100 Epochs)

**File:** `RFDETR-Medium-100-Epoch.pth`

Roboflow RF-DETR with DINOv2 backbone trained for 100 epochs. Achieves the highest precision but lower recall than YOLOv12.

| Metric | Score |
|---|---|
| mAP@0.5 | 0.773 |
| mAP@0.5:0.95 | 0.655 |
| Precision | 0.991 |
| Recall | 0.700 |
| Epochs | 100 |

---

## Classes

| ID | Class |
|---|---|
| 0 | Background |
| 1 | MountingHole |
| 2 | ComponentBody |
| 3 | SolderJoint |
| 4 | Lead |

---

## Usage

### Setup

```bash
git clone https://github.com/JC-prog/pcb-inspect-ai.git
cd pcb-inspect-ai/demo
pip install -r requirements.txt
```

### Download weights

```bash
huggingface-cli download JcProg/PCBInspect-AI --local-dir demo/checkpoint/
```

### Launch app

```bash
python app.py
```

Open [http://localhost:7860](http://localhost:7860), select a model in the **Model** tab, and run inference in the **Inference** tab.

---

## Training

### YOLOv12 Two-Stage Regime

- **Stage 1 (100 epochs):** 640x640 resolution, heavy augmentation for fast convergence
- **Stage 2 (60 epochs):** 896x896 resolution, lighter augmentation for fine-tuning

### RF-DETR

- **100 epochs** with DINOv2 backbone
- Bounding box annotations in COCO format

---

## Citation

If you use these weights, please reference the associated project:

```
PCBInspect-AI - Automated Generation of PCB Inspection Recipes Using Deep Learning-Based Feature Detection
National University of Singapore (NUS)
https://github.com/JC-prog/pcb-inspect-ai
```