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Inspection Engine

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...

KindOtherVersionRevision da69808

Model source

Kind
Other
File size
18.3–36.2 MB
Version
vRevision da69808
Updated
Sep 16, 2026
Downloads
0
Tasks
Object detection
Source
Hugging Face

Source description

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

FileDescription
best.ptBest PyTorch model weights
best.onnxONNX export for cross-platform/production deployment

Usage

PyTorch (Ultralytics)

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

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

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

Sources

1 source
Hugging FacePrimaryVersion da6980861ec2c74b7106e159a459a06823fe0398
huggingface.co/devanshty/Inspection-Engine/tree/da6980861ec2c74b7106e159a459a06823fe0398

Verified Sep 16

Model artifacts

2 artifacts

best.onnx

onnx · 36.2 MB · SHA-256 dc37a100d61b…eb12 · Hugging Face

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best.pt

pt · 18.3 MB · SHA-256 4e1babe0bd33…3763 · Hugging Face

Download

Source excerpts

2 excerpts

--- license: mit tags: - pytorch - onnx - object-detection - yolo - computer-vision - pcb-inspection - industrial --- # 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') ```

Source context: 0 downloads · 0 likes · Pipeline object-detection · Repo devanshty/Inspection-Engine