pcb-yolov8s-detection

PCB Object Detection - YOLOv8s

A fine-tuned YOLOv8s model for detecting 21 types of PCB (Printed Circuit Board) components. Trained for 90 epochs on a custom PCB dataset with 1,788 training images and 173 validation images.

Model Details

Performance Metrics (Epoch 69 - Best)

Per-Class [email protected]

21 PCB Classes

battery, button, buzzer, capacitor, clock, connector, diode, display, fuse, heatsink, ic, inductor, led, pads, pins, potentiometer, relay, resistor, switch, transformer, transistor

Training Configuration

task: detect
model: yolov8s.pt
epochs: 90
imgsz: 640
batch: 8
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
optimizer: auto
seed: 42
deterministic: true
amp: true
augmentations:
  hsv_h: 0.015
  hsv_s: 0.7
  hsv_v: 0.4
  fliplr: 0.5
  mosaic: 1.0
  erasing: 0.4

Usage

Installation

pip install ultralytics

Inference

from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("path/to/pcb_image.jpg", conf=0.25)

for result in results:
    boxes = result.boxes
    print(f"Detected {len(boxes)} objects")
    for box in boxes:
        cls = int(box.cls[0])
        conf = float(box.conf[0])
        xyxy = box.xyxy[0].tolist()
        print(f"  Class: {model.names[cls]}, Confidence: {conf:.3f}, Box: {xyxy}")

Export to ONNX

from ultralytics import YOLO

model = YOLO("best.pt")
model.export(format="onnx", imgsz=640, simplify=True)

Files

Training Results

Dataset

This model was trained on the Detecting the PCB Object dataset from Roboflow Universe.

• Training images: 1,788
• Validation images: 173
• Test images: Available
• Image size: 640x640

Citation

If you use this model, please cite:

@misc{pcb_object_detection_yolov8,
  title={PCB Object Detection using YOLOv8s},
  author={Arshia},
  year={2026},
  publisher={Hugging Face},
  note={Fine-tuned YOLOv8s model for 21-class PCB component detection}
}

License

This model is released under the MIT License.