AI Object Quality Inspection System
Budget: $30 – $250 USD
I need an image-recognition solution that can examine objects on our production line and flag defects in real time. The focus is strictly on objects—not faces or broad scene analysis—and every decision the model makes should support quality inspection.
What I’m looking for:
• A trained model (TensorFlow, PyTorch, or a comparable framework) that can differentiate acceptable products from faulty ones with high accuracy.
• A lightweight API or script that I can call from our existing line-side software to send an image and receive a pass/fail verdict plus confidence score.
• Clear documentation covering dataset preparation, model architecture, training parameters, and steps to retrain when new defect types appear.
• A brief demo—CLI or basic dashboard—that shows the system running on sample images.
I’ll supply an initial set of labeled photos; you’re welcome to augment it with synthetic data if it improves performance. Please outline your approach, expected accuracy, and any hardware or software assumptions so we can ensure the solution integrates smoothly on site.
What I’m looking for:
• A trained model (TensorFlow, PyTorch, or a comparable framework) that can differentiate acceptable products from faulty ones with high accuracy.
• A lightweight API or script that I can call from our existing line-side software to send an image and receive a pass/fail verdict plus confidence score.
• Clear documentation covering dataset preparation, model architecture, training parameters, and steps to retrain when new defect types appear.
• A brief demo—CLI or basic dashboard—that shows the system running on sample images.
I’ll supply an initial set of labeled photos; you’re welcome to augment it with synthetic data if it improves performance. Please outline your approach, expected accuracy, and any hardware or software assumptions so we can ensure the solution integrates smoothly on site.