Computer Vision Beyond the Demo

Computer vision demos look amazing. Real-world systems are harder. Here’s what actually works.

Lighting matters more than algorithms

Your model works great in perfect lighting. Real world lighting is messy. Test in the actual environment where you’ll deploy. Consider lighting as part of your system design.

Edge cases are everywhere

That object detection model? It works great on the demo images. Now try it on blurry photos, weird angles, and objects it’s never seen. Edge cases will break your system.

Preprocessing is half the battle

Image resizing, normalization, augmentation. These matter more than the model architecture. Get the preprocessing right, and the model becomes easier.

Start with pre-trained models

Don’t train from scratch unless you have a million images. Use models trained on ImageNet, COCO, or similar datasets. Fine-tune for your specific use case.

Test on real data

Your synthetic dataset looks perfect. Real data is messy. Test early and often on actual images from your use case.

Consider the full pipeline

Image capture, preprocessing, inference, post-processing, storage. The model is just one piece. Design the whole pipeline.

Data quality beats model complexity

A simple CNN on clean data beats a transformer on messy data. Focus on data quality first. Bad data will break even the best models.

Hardware constraints matter

Your model needs to run on actual hardware. Consider inference speed, memory usage, and power consumption. A model that takes 5 seconds to run isn’t useful for real-time applications.

Annotation is expensive

Labeling data costs money and time. Use active learning, weak supervision, and synthetic data to reduce annotation costs. Start with a small, high-quality dataset.

Domain adaptation is crucial

A model trained on one dataset might not work on another. Use domain adaptation techniques, or retrain on your specific data. The gap between training and deployment data is often larger than you think.

Evaluation metrics matter

Accuracy on a test set doesn’t tell the whole story. Consider precision, recall, F1-score, and domain-specific metrics. What matters for your use case?

Start simple, then optimize

Begin with a simple model and baseline. Get it working first, then optimize. Premature optimization is the root of all evil in computer vision too.

Real-world deployment challenges

  • Network latency: How fast does your model need to be?
  • Storage costs: How much data can you store?
  • Privacy concerns: Can you send images to external APIs?
  • Regulatory compliance: What are the legal requirements?

Common pitfalls to avoid

  1. Overfitting to demo data: Your model works great on the demo images, but fails on real data
  2. Ignoring edge cases: What happens when the camera is dirty or the lighting changes?
  3. Not considering the human in the loop: How do humans interact with your system?
  4. Optimizing the wrong metric: Accuracy might not be what actually matters

When computer vision actually works

  • Well-defined problems: Clear input, clear output
  • Good data quality: Clean, representative data
  • Appropriate complexity: Simple models for simple problems
  • Realistic expectations: Not every problem needs deep learning

Need help with computer vision systems? Let’s discuss your image processing challenges.