Training Parameter Optimization

Optimizing training parameters is essential for achieving high-performing computer vision models. This guide helps you understand and adjust key parameters.

Fundamental Parameters

Batch Size

• Definition: Number of samples processed before model parameter updates
• Recommendation:
• For GPUs with limited memory: 4-8
• For high-end GPUs: 16-32
• Impact: Affects training speed and generalization ability

Learning Rate

• Definition: Determines the magnitude of adjustments during optimization
• Recommendation: Start with 0.001 and use dynamic reduction
• Impact: Too high can cause divergence, too low can slow down learning

Number of Epochs

• Definition: Number of complete passes through the training dataset
• Recommendation:
• Light models: 50-100 epochs
• Complex models: 100-300 epochs
• Impact: Too few can undertrain, too many can overtrain

Advanced Parameters

Data Augmentation

Techniques to artificially diversify training data:
- Rotations (±15-30°)
- Horizontal flips
- Brightness variations (±20%)
- Random zoom (±20%)

Regularization Techniques

To prevent overfitting:
- Dropout (recommended: 0.2-0.5)
- Weight decay (recommended: 1e-4 to 1e-5)
- Early stopping (monitor validation loss)

Optimizers

• Adam: Good all-around choice, generally robust
• SGD with momentum: Can outperform Adam for some tasks, but requires more tuning
• AdamW: Recommended for larger models

Optimization Strategies

1. Start simple: Use default parameters
2. Cross-validation: Test different combinations on subsets
3. Grid search: To methodically explore the parameter space
4. Random search: More efficient than grid search for many parameters
5. Bayesian optimization: For advanced automated exploration

By optimizing these parameters, you can significantly improve the performance of your computer vision models on Techsolut Vision.