Introduction
After working on dozens of computer vision projects, I’ve seen teams make the same mistakes repeatedly. Here are the top 5 pitfalls and how to avoid them.
1. Insufficient Training Data
The Problem
Many teams start with only a few hundred images, expecting deep learning magic to happen.
The Solution
- Minimum viable dataset: 1000+ images per class
- Use data augmentation: Rotation, flipping, color jitter
- Synthetic data generation: When real data is scarce
- Active learning: Iteratively add hard examples
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from albumentations import (
Compose, Rotate, RandomBrightnessContrast,
HorizontalFlip, GaussNoise
)
transform = Compose([
Rotate(limit=30),
HorizontalFlip(p=0.5),
RandomBrightnessContrast(p=0.2),
GaussNoise(p=0.1)
])
2. Ignoring Class Imbalance
The Problem
Real-world defects are rare. Your dataset might have 95% “good” images and only 5% defects.
The Solution
- Weighted loss functions
- Oversampling minority class
- Focal loss for hard examples
- Synthetic minority oversampling (SMOTE)
3. Not Testing on Production-Like Data
The Problem
Model performs great in lab conditions but fails in production due to:
- Different lighting
- Camera angles
- Background noise
- Motion blur
The Solution
- Collect data from actual production line
- Test with various lighting conditions
- Simulate real-world conditions in training
- Continuous monitoring and retraining
4. Over-Optimizing for Accuracy
The Problem
Chasing 99% accuracy when 95% is sufficient, adding complexity and computational cost.
The Solution
- Define success metrics clearly: False positives vs false negatives
- Consider business impact: What’s the cost of each error type?
- Optimize for production constraints: Speed, memory, power
- Use appropriate metrics: Precision, recall, F1, not just accuracy
5. Neglecting Model Interpretability
The Problem
Black-box models make it hard to debug failures and gain stakeholder trust.
The Solution
- Use Grad-CAM for visualization
- Feature importance analysis
- Document failure cases
- Implement confidence scores
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from pytorch_grad_cam import GradCAM
# Visualize what the model is looking at
cam = GradCAM(model=model, target_layers=[model.layer4[-1]])
grayscale_cam = cam(input_tensor=image)
Bonus Tips
Model Deployment
- Start simple (classical CV before deep learning)
- Version your datasets and models
- A/B test in production
- Build feedback loops
Data Quality
- Manual review of edge cases
- Regular data audits
- Clear labeling guidelines
- Multiple annotators for validation
Conclusion
Success in computer vision comes from:
- Quality data over quantity (though you need both!)
- Production-focused development
- Iterative improvement based on real feedback
- Clear metrics aligned with business goals
Avoid these mistakes, and you’ll save months of frustration!
What mistakes have you encountered? Share in the comments below!
Discussion