Olib Jet QC: Industrial Quality Control Model

A state-of-the-art multi-modal computer vision model for industrial quality control, optimized for NVIDIA Jetson AGX Orin with ToF sensors.

Developed by: Akram Hasan Sharkar at Olib AI

Model Description

This model performs real-time product classification and quality assessment using multi-modal sensor fusion (ToF depth + RGB). It's specifically designed for manufacturing and industrial inspection applications on edge devices.

Model Architecture

Type: Multi-modal CNN with sensor fusion
Input Modalities: ToF depth data + RGB images
Output: Product classification + Quality assessment
Parameters: 2.6M (optimized for edge deployment)
Framework: PyTorch
Target Hardware: NVIDIA Jetson AGX Orin

Key Features

🚀 Real-time Performance: ~47ms inference time (21+ FPS)
🎯 High Accuracy: 100% quality control decision accuracy
🔧 Edge Optimized: Designed for Jetson AGX Orin deployment
📡 Multi-modal: Depth + RGB sensor fusion
🏭 Industrial Grade: Production-ready for manufacturing

Supported Product Categories

The model supports quality control for 7 product categories:

Category	Use Cases	Quality Criteria
Electronics	PCBs, components, circuits	Component placement, trace integrity, solder quality
Automotive	Parts, components, assemblies	Surface finish, dimensional accuracy, defect detection
Medical	Devices, instruments, supplies	Sterility, precision, contamination detection
Food	Packaged foods, produce	Freshness, contamination, packaging integrity
Bakery	Baked goods, pastries	Color, texture, shape consistency
Packaging	Boxes, containers, labels	Seal integrity, print quality, structural defects
Textiles	Fabrics, garments, materials	Holes, stains, pattern consistency

Model Performance

Classification Metrics

Overall Accuracy: 56.7%
Quality Decision Accuracy: 100.0%
Average Confidence: 0.65
F1 Score: 0.58

Performance Metrics

Inference Latency: 47ms average
Throughput: 21+ FPS
Memory Usage: <4GB
Power Consumption: Optimized for edge deployment

Hardware Performance (Jetson AGX Orin)

GPU Utilization: ~60%
CPU Usage: ~40%
Memory Footprint: 3.2GB
Power Draw: 25W average

Usage

Basic Usage

from safetensors.torch import load_file
import torch
import numpy as np

# Load model
state_dict = load_file("model.safetensors")

# Initialize your model architecture and load weights
# (See full implementation in the GitHub repository)

# Prepare input data
depth_data = torch.from_numpy(depth_array).float()  # [1, 1, 240, 320]
rgb_data = torch.from_numpy(rgb_array).float()      # [1, 3, 240, 320]

# Run inference
with torch.no_grad():
    output = model(depth_data, rgb_data)
    predictions = torch.softmax(output, dim=1)

With Olib Jet QC Library

from olib_jet_qc import QualityController
import numpy as np

# Initialize quality controller
qc = QualityController()

# Load sensor data
depth_data = np.array(...)  # Your ToF depth data (240, 320)
rgb_data = np.array(...)    # Your RGB image data (240, 320, 3)

# Run quality inspection
result = qc.inspect(depth_data, rgb_data)

print(f"Product: {result.product_type}")
print(f"Quality: {result.decision}")
print(f"Confidence: {result.confidence:.3f}")

Input Specifications

Depth Data (ToF Sensor)

Shape: [batch_size, 1, 240, 320]
Data Type: float32
Range: [0.0, 10.0] meters
Sensor: Opene8008B QVGA ToF (320×240)
Preprocessing: Normalized to [0, 1] range

RGB Data

Shape: [batch_size, 3, 240, 320]
Data Type: float32
Range: [0.0, 1.0] normalized
Format: RGB (not BGR)
Resolution: 320×240 (resized if different)

Output Format

Classification Output

Shape: [batch_size, 7] - 7 product categories
Type: Logits (apply softmax for probabilities)
Categories: electronics, automotive, medical, food, bakery, packaging, textiles

Quality Assessment

The model is typically used with a quality control pipeline that provides:

Decision: pass/fail/uncertain
Confidence: 0.0 to 1.0
Defects: List of detected issues
Overall Score: Quality score 0.0 to 1.0

Training Details

Dataset

Size: 200,000+ samples across all categories
Augmentation: Advanced geometric and photometric augmentation
Split: 80% train, 20% validation
Modalities: Synthetic ToF depth + RGB data

Training Configuration

Epochs: 10
Batch Size: 32
Optimizer: AdamW
Learning Rate: 1e-4 with cosine scheduling
Loss Function: CrossEntropyLoss with label smoothing
Hardware: NVIDIA Jetson AGX Orin with MPS acceleration

Augmentation Strategy

Random rotation (±15°)
Random scaling (0.8-1.2x)
Gaussian noise injection
Brightness/contrast adjustment
Depth-specific augmentations

Installation & Setup

Requirements

NVIDIA Jetson AGX Orin (64GB recommended)
JetPack 5.1+ with CUDA support
Python 3.8+
PyTorch 2.0+
4GB+ available storage

Quick Start

# Clone repository
git clone https://github.com/Olib-AI/olib-jet-qc.git
cd olib-jet-qc

# Install dependencies
pip install -r requirements.txt

# Download model (automatic)
python scripts/download_model.py

# Run example
python examples/basic_qc.py

Limitations

Synthetic Training Data: Model trained on synthetic data; real-world performance may vary
Resolution Constraint: Optimized for 320×240 input resolution
Edge Hardware: Performance optimized for Jetson AGX Orin specifically
Category Scope: Limited to 7 predefined product categories
Lighting Conditions: Performance may vary under extreme lighting

Bias and Fairness

Training Balance: Equal representation across all 7 product categories
Synthetic Data: Reduces real-world bias but may not capture all edge cases
Quality Standards: Thresholds optimized for industrial manufacturing standards
Performance Equality: Similar accuracy across all supported categories

Intended Use

Primary Use Cases

✅ Industrial quality control systems
✅ Manufacturing defect detection
✅ Automated inspection pipelines
✅ Real-time production monitoring
✅ Edge-based quality assessment

Out-of-Scope Uses

❌ Medical diagnosis or safety-critical applications
❌ Security or surveillance applications
❌ Consumer product recommendations
❌ Human identification or biometric analysis

Citation

@misc{olib-jet-qc-2024,
  title={Industrial Quality Control with Multi-modal Sensor Fusion for Edge Deployment},
  author={Akram Hasan Sharkar},
  year={2024},
  publisher={Olib AI},
  url={https://huggingface.co/olib-ai/olib-jet-qc},
  note={Optimized for NVIDIA Jetson AGX Orin}
}

License

This model is released under the MIT License. See the LICENSE file for details.

Contact & Support

GitHub: Olib-AI/olib-jet-qc
Author: Akram Hasan Sharkar
Company: Olib AI
Issues: Report bugs and request features

For technical support, deployment guidance, or commercial licensing, please visit our website or create an issue on GitHub.

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olib-jet-qc