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What are the best ways to address overfitting and underfitting in computer vision models?

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Overfitting and underfitting are common challenges in computer vision models. Overfitting occurs when the model learns too much from the training data and fails to generalize to new or unseen data. Underfitting occurs when the model learns too little from the training data and performs poorly on both the training and the test data. In this article, you will learn some of the best ways to address these issues and improve your computer vision model's accuracy and robustness.

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Hayden Cordeiro

Data Engineer Co-op @ RBC | Masters of Applied Computing | Ex Browserstack | Full Stack Developer | Experienced SDET |…
Abdelrahman Selim

Technical Team Lead ( Views are my own )

1 Use proper data preprocessing

Data preprocessing is an essential step in computer vision projects, and involves transforming, cleaning, and augmenting the data to make it suitable for the model. Resizing and cropping images to a uniform size and aspect ratio, normalizing pixel values, applying data augmentation techniques such as rotation, flipping, zooming, shifting, and adding noise, as well as splitting the data into training, validation, and test sets with a balanced distribution of classes and samples are some of the common data preprocessing techniques. This process can help reduce overfitting by preventing the model from memorizing specific features or patterns in the data, while also reducing underfitting by providing more information and variation to the model.

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Hayden Cordeiro

Data Engineer Co-op @ RBC | Masters of Applied Computing | Ex Browserstack | Full Stack Developer | Experienced SDET | Automation Testing | Software Engineering
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To tackle overfitting and underfitting in computer vision models effectively, it's important to incorporate domain-specific knowledge. This means leveraging insights from the specific field the model is intended for. By doing so, we can more accurately identify and address biases and limitations. This approach ensures that the model performs well not only in theoretical scenarios but also in real-world applications, where nuances and complexities are paramount considerations for success.

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2 Choose the right model architecture

The model architecture refers to the design and configuration of the layers, neurons, activation functions, and parameters of the model. Choosing the right model architecture can have a major effect on the performance and generalization of the model. When selecting a model architecture, one must consider factors such as the complexity and size of the data, the type and objective of the task, and the availability of pre-trained models. For instance, convolutional neural networks (CNNs) are often employed for computer vision tasks since they can process spatial information and extract hierarchical features from images. Pre-trained models, which have been trained on large datasets such as ImageNet, can help reduce overfitting and underfitting by utilizing knowledge and weights learned from prior tasks.

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3 Apply regularization techniques

Regularization techniques are methods that modify the model or the learning process to reduce overfitting and improve generalization. Some of the most popular techniques include weight decay or L2 regularization, which encourages the model to use smaller and simpler weights; dropout, which randomly drops out a fraction of neurons or units in a layer; batch normalization, which normalizes the inputs of each layer; and early stopping, which stops the training process when the validation loss stops improving or starts increasing. These techniques help create a more robust and diverse ensemble of models, reduce internal covariate shift, and save the best model based on validation performance.

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Abdelrahman Selim

Technical Team Lead ( Views are my own )
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For a movie app, L2 regularization prevents over-reliance on niche features, ensuring broad appeal. In facial recognition, dropout boosts robustness against varying conditions. Batch normalization helps voice assistants quickly adapt to accents by stabilizing inputs. Early stopping in predictive maintenance optimizes performance without overfitting to historical data, predicting failures reliably. These techniques enhance real-world model performance by preventing overfitting, ensuring they work well across diverse scenarios.

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4 Evaluate and fine-tune the model

The final step in addressing overfitting and underfitting is to evaluate and fine-tune the model using appropriate metrics and techniques. You should choose the right evaluation metrics that reflect the goal and difficulty of the task, such as accuracy, precision, recall, and F1-score for classification tasks, or mean average precision (mAP), intersection over union (IoU), and mean IoU for detection and segmentation tasks. Additionally, you should use cross-validation or hold-out sets to split the data into multiple subsets and use some of them for training and some of them for testing. This ensures that the model is evaluated on unseen data and reduces the bias and variance of the estimates. Finally, you should fine-tune the model parameters by adjusting the learning rate, number of epochs, batch size, and other hyperparameters to optimize model performance. This can be done manually or using more advanced methods like Bayesian optimization or genetic algorithms. By following these best practices, you can address overfitting and underfitting in your computer vision models to achieve better results and reliability.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Abdelrahman Selim

Technical Team Lead ( Views are my own )
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Besides regularization, consider data augmentation (creating variations of training data), feature engineering (selecting informative attributes), and ensemble methods (combining multiple models for better predictions). Hyperparameter tuning (optimizing model settings) also plays a crucial role. Ensuring a diverse and representative dataset is vital to prevent bias. Cross-validation techniques help assess model generalizability. Balancing model complexity with interpretability ensures practical usability. These strategies, along with regularization, form a comprehensive approach to building robust, effective machine learning models.

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What are the best ways to address overfitting and underfitting in computer vision models?

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1 Use proper data preprocessing

2 Choose the right model architecture

3 Apply regularization techniques

4 Evaluate and fine-tune the model

5 Here’s what else to consider

Software Development

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What are the best ways to address overfitting and underfitting in computer vision models?

1

2

3

4

5

1 Use proper data preprocessing

2 Choose the right model architecture

3 Apply regularization techniques

4 Evaluate and fine-tune the model

5 Here’s what else to consider

Software Development

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