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README.md

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+---
+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
+```