Solving Convergence Problems in Model Training

This troubleshooting guide helps you diagnose and solve common convergence problems that can occur when training computer vision models. It covers typical symptoms, their likely causes, and offers practical solutions.

Common Symptoms of Convergence Problems

• Loss Plateaus - The loss function stops decreasing after a few epochs
• Fluctuating Loss - Significant variations in loss between batches or epochs
• Exploding Loss - Loss values that suddenly increase dramatically
• Very Slow Decreasing Loss - Minimal progress despite many epochs
• NaN (Not a Number) - Loss becomes NaN during training
• Poor Performance - The model trains but with performance well below expectations

Diagnosis and Solutions by Problem Category

1. Data-Related Problems

Symptom: Poor performance or early plateauing

Potential Causes

• Insufficient Data - Too few examples to generalize
• Imbalanced Data - Some classes are underrepresented
• Incorrect Annotations - Errors in labels or bounding boxes
• Inadequate Preprocessing - Missing or incorrect normalization
• Limited Variability - Data too similar not covering the problem space

Solutions

1. Increase Dataset Size
2. Collect more images if possible
3. Use data augmentation (rotation, flip, lighting changes, etc.)

4. Leverage transfer learning on a pre-trained model

5. Manage Class Imbalances

6. Use class weighting in the loss function
7. Apply oversampling/undersampling techniques

8. Use techniques like SMOTE for minority classes

9. Check Your Annotations

10. Examine a random sample of annotations for errors
11. Use Techsolut's annotation validation tool

12. Fix inconsistencies in annotation styles

13. Improve Preprocessing

14. Normalize images according to your dataset statistics
15. Verify that transformations preserve relevant information
16. Standardize resolution and input formats

2. Hyperparameter-Related Problems

Symptom: Slow learning, significant fluctuations, or gradient explosions

Potential Causes

• Inappropriate Learning Rate - Too high or too low
• Inappropriate Batch Size - Too large or too small
• Poor Weight Initialization - Initialization unsuitable for the architecture
• Inadequate Optimizer Choice - Optimizer poorly adapted to the problem

Solutions

1. Adjust Learning Rate
2. For stagnating loss: slightly increase learning rate
3. For exploding or fluctuating loss: reduce learning rate
4. Use a learning rate schedule (cosine, step, etc.)

5. Try the "learning rate finder" technique to find the optimal value

6. Experiment with Batch Size

7. Increase size if training is unstable
8. Reduce size if generalization is poor

9. Balance GPU memory constraints with training stability

10. Modify Weight Initialization

11. Use appropriate initializations such as He or Xavier/Glorot

12. Ensure initial standard deviations are appropriate for the architecture

13. Try Different Optimizers

14. Adam: good general choice to start
15. SGD with momentum: often better final generalization
16. AdamW: Adam with adapted weight decay

3. Model-Related Problems

Symptom: Underfitting or rapid overfitting

Potential Causes

• Unsuitable Model Capacity - Too simple or too complex
• Inappropriate Architecture - Structure unsuited to the problem
• Poor Regularization - Too strong or too weak
• Gradient Problems - Vanishing or exploding gradients

Solutions

1. Adjust Model Capacity
2. If underfitting: increase network depth or width
3. If overfitting: reduce size or add regularization

4. Try different architectures or size variants (S, M, L)

5. Implement Appropriate Regularization

6. Add dropout (start with 0.1-0.3)
7. Use L1 or L2 regularization (weight decay)
8. Implement batch normalization or layer normalization

9. Try techniques like label smoothing

10. Address Gradient Problems

11. Use residual connections (skip connections)
12. Apply gradient clipping to avoid explosions
13. Use modern activation functions (SiLU/Swish, Mish)
14. Check that gradients aren't vanishing with the profiling tool

4. Loss Function-Related Problems

Symptom: Disappointing performance despite good loss convergence

Potential Causes

• Misaligned Loss Function - Doesn't reflect evaluation metric well
• Poorly Balanced Loss - In multi-objective tasks
• Unstable Loss Function - Sensitive to outliers

Solutions

1. Choose an Appropriate Loss Function
2. For object detection: CIoU or DIoU instead of standard IoU
3. For imbalanced classification: Focal Loss instead of Cross-Entropy

4. For segmentation: Dice Loss or Combo Loss (BCE + Dice)

5. Balance Loss Components

6. Adjust coefficients in composite losses
7. Normalize each component so they have similar scales

8. Use uncertainty-based multi-task learning (Uncertainty-based Weighting)

9. Handle Difficult Examples

10. Implement hard negative mining
11. Use curriculum learning techniques (start with easy examples)
12. Apply techniques robust to outliers (Huber Loss, etc.)

5. Technical and Hardware Problems

Symptom: Unexpected errors, inconsistent behavior, crashes

Potential Causes

• GPU/Memory Issues - Memory overflows or inconsistent calculations
• Software Bugs - Errors in code or libraries
• Numerical Precision - Issues related to floating-point calculation precision

Solutions

1. Optimize GPU Usage
2. Reduce batch size or image resolution
3. Use mixed precision training
4. Monitor GPU memory during training

5. Avoid memory leaks by freeing unused tensors

6. Check Your Code

7. Examine tensor shapes at critical points
8. Ensure preprocessings are correctly applied

9. Confirm library version compatibility

10. Address Numerical Issues

11. Avoid divisions by zero and logarithms of negative numbers
12. Add small constants (epsilon) to denominators
13. Use numerically stable operations (log_softmax vs log(softmax))

Recommended Diagnostic Workflows

For Early Plateauing

1. Check data quality and quantity first
2. Try a higher learning rate
3. Verify that the model has sufficient capacity
4. Visually inspect predictions on the training dataset

For Exploding Losses (NaN)

1. Drastically reduce learning rate
2. Implement gradient clipping
3. Check for extreme values in your data
4. Ensure normalization is correctly applied

For Instability (Significant Fluctuations)

1. Increase batch size
2. Reduce learning rate
3. Try a more robust optimizer like AdamW
4. Implement Exponential Moving Average (EMA) of weights

For Rapid Overfitting

1. Increase regularization (dropout, weight decay)
2. Reduce model capacity
3. Add more data or use more augmentation
4. Stop training earlier (early stopping)

Techsolut Diagnostic Tools

Use the tools built into the platform to effectively diagnose your problems:

Gradient Visualization

1. Access the "Diagnostics" tab of your training
2. Select "Gradient Analysis"
3. Examine gradient distribution by layer
4. Look for signs of vanishing/exploding gradients

Activation Tracking

1. Enable activation tracking in training parameters
2. Observe the distribution of activation values by layer
3. Verify that activations aren't saturating (too many 0s or extreme values)

Problematic Examples Analysis

1. Use the "Difficult Examples" feature
2. Identify images that generate high losses
3. Look for common patterns among these examples
4. Fix annotations or increase the number of similar examples

Conclusion

Convergence problems are often multi-factorial and may require several simultaneous adjustments. Keep a systematic approach and modify one parameter at a time to understand its impact. Document your experiments in Techsolut's "Experiment Notes" section to keep track of your trials.

If you encounter persistent problems despite these tips, don't hesitate to contact our technical support team who can analyze your specific case in more detail.