You can deploy this model and start earning money today!

devanshty/
Inspection-Engine

Views

No views yet

Inspection Engine

Model Description

Inspection Engine is a YOLO11s model fine-tuned for automated PCB (Printed Circuit Board) defect detection. It identifies manufacturing defects such as missing components, solder bridges, lifted pads, and other anomalies in real-time, enabling automated quality control in electronics manufacturing.

Model Architecture

Base Model: YOLO11s (Small variant — optimized for speed)
Framework: PyTorch + ONNX (for cross-platform deployment)
Task: Object Detection (Defect Localization)
Input: High-resolution PCB images

Training Details

Dataset: PCB defect inspection dataset with annotated defect regions
Checkpoint: inspection_engine_final3 (best performing run)
Augmentations: Mosaic, color jitter, random affine transforms
Export: Exported to ONNX for production deployment

Files

File	Description
`best.pt`	Best PyTorch model weights
`best.onnx`	ONNX export for cross-platform/production deployment

Usage

PyTorch (Ultralytics)

python

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.pt')
model = YOLO(model_path)
results = model('pcb_image.jpg')
results[0].show()

ONNX Runtime

python

import onnxruntime as ort
import numpy as np
from huggingface_hub import hf_hub_download

onnx_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.onnx')
session = ort.InferenceSession(onnx_path)
# Prepare input (1, 3, H, W) float32 normalized
input_name = session.get_inputs()[0].name
outputs = session.run(None, {input_name: np.zeros((1, 3, 640, 640), dtype=np.float32)})

Download & Use

python

from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id='devanshty/Inspection-Engine', filename='best.pt')