Here are some additional tips to keep in mind while optimizing computer vision models:
The Effect of Learning Rate
- Image Complexity: Datasets with intricate details or subtle patterns often benefit from lower learning rates. This prevents the model from jumping over important features.
- Transfer Learning: When fine-tuning a pre-trained model, significantly lower learning rates are usually necessary to avoid damaging existing knowledge.
- Visualizing Progress: Plot image predictions along with your loss curves. Your learning rate might be too high if predictions degrade sharply as the loss drops.
Optimizers and Learning Dynamics
- Task-Specific Behavior: In image segmentation tasks, Stochastic Gradient Descent (SGD) can sometimes outperform fancier optimizers due to its noisier updates helping it escape local optima.
- Adaptive Optimizers: Adam and its variants are generally good starting points for computer vision. However, experimenting with others like AdaGrad or RMSprop can sometimes yield improvements.
- Regularization for Visual Data: Regularization techniques like dropout, spatial dropout, L1 and L2 are essential. Data augmentation also acts as a potent regularizer. We discussed dropout in the previous module, but what are L1 and L2 regularizations?
- L1 regularization, also known as Lasso (Least Absolute Shrinkage and Selection Operator) regression, adds a penalty term to the loss function equal to the absolute value of the magnitude of coefficients. This type of regularization can lead to sparse models by driving some coefficients to exactly zero, effectively performing feature selection. This can eliminate the impact of less important features and works well when you have a high number of learned features that you suspect are not relevant.
- L2 regularization, also known as Ridge regression, adds a penalty term to the loss function equal to the square of the magnitude of coefficients. This type of regularization discourages large coefficients but does not set them to zero, instead it makes them small but non-zero. This reduces the impact of less important learned features but keeps all of them in case they are potentially relevant.
Dataset Considerations and Regularization
- Class Imbalance: Smaller learning rates and oversampling the minority class are often needed to prevent the model from getting stuck on the majority class.
- Data Augmentations: Aggressive augmentation can reduce the need for strong regularization. This is because the model is “seeing” essentially novel images while it learns, helping to prevent overfitting by memorizing the training data. The opposite is also true – if your augmentation pipeline is weak, you may need heavier regularization.
- *Finding the Sweet Spot: The ideal regularization strength depends heavily on model architecture, dataset complexity, and the augmentations used.
Additional Considerations
- Batch Size: Computer vision models often use smaller batch sizes due to memory constraints. This makes training noisier, which in turn, impacts learning rate and optimizer choices.
- Image Resolution: Training with higher-resolution images may necessitate slower learning rates and stronger regularization. Higher-resolution images contain much more fine-grained detail and complexity, leading to a greater number of possible patterns for the model to learn. This increases the risk of overfitting, where the model memorizes these details rather than generalizing well to unseen data. Techniques like Dropout, L2 Regularization, Early Stopping can help with overfitting. If the model is especially deep to increase capacity, consider adding Normalization layers.
Return to Module 3 or Continue to Comprehensive Model Evaluation
