10 Common Pitfalls When Building a Computer Vision Model

In collaboration with Tamunotonye Harry

Introduction

Have you ever experienced the thrill of building a cutting-edge Computer Vision model, only to face unexpected challenges that threaten to derail your progress? Whether you’re a seasoned computer vision engineer or just starting on this captivating journey, it’s crucial to be aware of the common pitfalls that can arise during the model-building process.

This article will delve into uncovering the ten most prevalent pitfalls that can impede your progress in development and deployment. But fret not! We won’t leave you stranded. Instead, we will provide practical insights, tips, and techniques to help you overcome these obstacles and confidently navigate the intricate landscape of Computer Vision.

10 Common Pitfalls When Building a Computer Vision Model

Photo by Kevin Ku on Unsplash

Before we dive in, let me share a brief story: Picture a dedicated team tirelessly working on an image recognition system for an autonomous vehicle. Despite countless development hours, they were disheartened to discover that their model struggled to recognize crucial road signs in challenging lighting conditions. Frustrated, they realized they had fallen into a common pitfall that could have been avoided with the proper knowledge. Now, let’s embark on this journey together and equip ourselves with the expertise to navigate these pitfalls confidently.

Pitfall 1: Underestimating the project scope

Before beginning a computer vision project, it is crucial for the team of data scientists to carefully assess the requirements, metrics, and scope and employ effective project management frameworks to ensure a smooth process. Let’s take a closer look at an example to illustrate this point.

Consider a second-hand car dealership that is enthusiastic about implementing AI technology in their business. They envision a system where customers can use their smartphones to view a car, access detailed information about the vehicle, and see the proposed price. They aim to build a computer vision model capable of detecting the car’s make and providing pricing specifications to achieve this.

Initially, the project seemed exciting. However, the team soon realizes that they failed to consider several crucial factors, such as:

  • Availability of a comprehensive, publicly annotated car database covering various car models and how to access them.
  • Incorporating additional car data that includes instances of scratches to enable the model to detect such imperfections.
  • Determining if estimating the car’s mileage is possible based on visual cues.

These oversights can lead to significant delays and unforeseen costs. However, these challenges can be mitigated by reframing the problem statement, establishing precise requirements with product metrics in mind, and visualizing the different stages of product creation to ensure alignment among team members. By iterating on the problem statement and narrowing the scope, the team can tackle the project with greater clarity and purpose. For instance, starting with a more focused problem statement like “Creating a model that can detect all Mercedes cars and provide pricing” allows for a more manageable initial phase before expanding further.

Photo by Andrea Piacquadio

Photo by Andrea Piacquadio

Implementing effective project management strategies is crucial to overcome the pitfall of underestimating project scope in computer vision projects. Here are key strategies to ensure success:

  • Requirement Elicitation: Engage stakeholders to understand their needs, capture detailed requirements, and validate them iteratively through feedback.
  • Documentation: Maintain clear and structured documentation of project requirements, plans, and progress. Utilize visual aids for better understanding and communication.
  • Effective Communication: Establish clear communication channels, schedule regular meetings and updates, encourage open discussions, and utilize collaborative tools for real-time communication and document sharing. Address stakeholder needs and concerns.
  • Define Clear Project Scope: Clearly define project boundaries, objectives, deliverables, and timelines. Break down the project into manageable tasks and create a roadmap.
  • Prioritize and Sequence Tasks: Identify critical tasks based on dependencies and available resources—track task progress using project management tools and methodologies.
  • Establish Milestones and KPIs: Set milestones to assess progress and ensure goal alignment. Define relevant KPIs to measure project success and monitor performance.
  • Allocate Resources Effectively: Assess and allocate resource requirements based on project needs and team capacities.

By following these strategies, you can mitigate risks, ensure effective communication, and successfully manage the scope of your computer vision project.

Pitfall 2: Faulty data labeling and annotation process

Data leakage is a significant concern in computer vision tasks, where unintentionally including inappropriate information in the training data can compromise the model’s performance during deployment or inference. This occurs when the model inadvertently learns from data that contains details about the target variable that would not be accessible in real-world scenarios. Data leakage can lead to inflated model performance during development and severely impact the accuracy and reliability of the model in practical applications.

For instance, let’s consider a computer vision model trained to classify images of cats and dogs. During the data preprocessing phase, the model accidentally includes metadata about the image sources, such as the file names, image resolutions, or timestamps. These metadata features contain information that should not be available to the model during prediction, as it simulates a real-world scenario where such details would not be known. If the model learns to rely on these leaked features, it may perform exceptionally well during development. However, when deployed to classify new images in real-time, it will fail to generalize accurately because it relies on leaked information that wouldn’t be available in practice.

To mitigate the risk of data leakage in computer vision tasks, it is crucial to carefully examine the training data and ensure that only relevant and realistic information is used for model training. This involves thorough data preprocessing, excluding features that contain leakage-prone information—for example, removing metadata or any other data that would not be available during inference. It’s essential to preprocess the data in a manner that mirrors the conditions of real-world scenarios where the model will be deployed. Additionally, rigorous validation and testing procedures can help detect and prevent data leakage. During the validation process, evaluating the model’s performance on unseen data closely resembles the real-world distribution is important. If the model’s performance is disproportionately high compared to expectations, it may indicate potential data leakage, and further investigation is required. Furthermore, ensuring a clear separation of data preprocessing steps for the training and test sets is crucial. This helps prevent the contamination of the test set with leaked information from the training data, ensuring that the model’s performance is evaluated on unseen data accurately.

Pitfall 3: Data leakage

Data leakage is a significant concern in computer vision tasks, where unintentionally including inappropriate information in the training data can compromise the model’s performance during deployment or inference. This occurs when the model inadvertently learns from data that contains details about the target variable that would not be accessible in real-world scenarios. Data leakage can lead to inflated model performance during development and severely impact the accuracy and reliability of the model in practical applications.

For instance, let’s consider a computer vision model trained to classify images of cats and dogs. During the data preprocessing phase, the model accidentally includes metadata about the image sources, such as the file names, image resolutions, or timestamps. These metadata features contain information that should not be available to the model during prediction, as it simulates a real-world scenario where such details would not be known. If the model learns to rely on these leaked features, it may perform exceptionally well during development. However, when deployed to classify new images in real-time, it will fail to generalize accurately because it relies on leaked information that wouldn’t be available in practice.

To mitigate the risk of data leakage in computer vision tasks, it is crucial to carefully examine the training data and ensure that only relevant and realistic information is used for model training. This involves thorough data preprocessing, excluding features that contain leakage-prone information—for example, removing metadata or any other data that would not be available during inference. It’s essential to preprocess the data in a manner that mirrors the conditions of real-world scenarios where the model will be deployed. Additionally, rigorous validation and testing procedures can help detect and prevent data leakage. During the validation process, evaluating the model’s performance on unseen data closely resembles the real-world distribution is important. If the model’s performance is disproportionately high compared to expectations, it may indicate potential data leakage, and further investigation is required. Furthermore, ensuring a clear separation of data preprocessing steps for the training and test sets is crucial. This helps prevent the contamination of the test set with leaked information from the training data, ensuring that the model’s performance is evaluated on unseen data accurately.

Pitfall 4: Unmindful of data bias

Data bias refers to systematic errors or prejudices in training data, which can result in biased outcomes or predictions. When the training data contains imbalances, inaccuracies, or discriminatory patterns, the resulting models may perpetuate and amplify these biases, leading to unfair or discriminatory predictions. Therefore, it is crucial to be mindful of data bias and proactively take steps to mitigate its impact on computer vision projects.

For example, let’s consider a facial recognition system used for criminal identification. If the training data predominantly consists of images of individuals from specific demographics (e.g., primarily males of a particular race), the resulting model may exhibit bias toward those groups. Consequently, when the system is deployed to identify criminals, it may disproportionately misidentify individuals from underrepresented demographics, leading to biased and unfair outcomes.

Some of the sources of biases you should look out for when working on computer vision projects include:

  • Selection bias: When you train your CV model on only a skewed subset of scenarios it may encounter in production.
  • Measurement bias: When there are differences in the way you collect image data for training and the way production data will be collected for model prediction.
  • Confirmation bias: When the data collection method mirrors real-world biases and value distribution in real life, the model reinforces undesirable behaviors.

To address data bias, it is crucial to ensure diverse and representative training datasets. This involves collecting data from various sources and demographics to minimize the risk of bias. Additionally, implementing bias detection and mitigation techniques, such as data augmentation, careful feature selection, and algorithmic fairness measures, can help identify and correct biased patterns in the data. Regularly monitoring and evaluating the model’s performance for bias is also essential, allowing for ongoing improvements and corrections to ensure fair and unbiased outcomes. Detecting bias and understanding its origins are key steps in resolving biases. One effective technique is data slicing evaluation. You can study the model’s behavior on each slice or subset by slicing or subsetting the dataset. This enables you to identify groups where the sliced metrics significantly differ from the overall dataset, highlighting potential biases requiring attention and remediation. By being mindful of data bias and actively working to mitigate its impact, computer vision projects can develop more accurate, reliable, and fair models, promoting equitable outcomes in various real-world applications.

Pitfall 5: Poor model evaluation and testing

Inadequate evaluation and testing practices can significantly undermine the effectiveness and reliability of computer vision projects. Without thorough evaluation and testing, models may suffer from inaccurate performance assessment, limited generalization capabilities, and an increased risk of deploying flawed models in real-world applications.

For instance, let’s consider a computer vision project focused on detecting and classifying diseases in medical images. If the model evaluation relies solely on performance metrics calculated from the training data, it can provide a misleading impression of the model’s effectiveness. The model may exhibit high accuracy on the training set but fail to generalize well to new, unseen data. This lack of rigorous evaluation and testing, particularly on separate validation and test sets, can lead to incorrect diagnoses and potential harm to patients.

To avoid this pitfall, it is crucial to establish thorough evaluation and testing processes. This starts with appropriately partitioning the data into training, validation, and testing sets. During training, the model should be evaluated on the validation set, enabling informed decisions regarding hyperparameters and model selection. Once the model is finalized, rigorous testing on a separate test set should be conducted to assess its performance on unseen data. Employ a range of evaluation metrics to provide a more comprehensive understanding of the model’s strengths and weaknesses. Different models may require specific evaluation metrics, so it is important to research and identify the most suitable approaches for the specific algorithm being applied.

To avoid pitfalls in model evaluation and testing, consider the following steps:

  • Familiarize yourself with the algorithm being applied and determine the best practices for evaluating its performance.
  • Test the model on unseen test data to ensure it meets the desired performance metrics. For example, if the model’s speed is crucial, evaluate whether it meets the required time constraints.
  • Leverage libraries and tools designed for efficient model evaluation. For example, the Deepchecks library simplifies the evaluation process for machine learning engineers and data scientists, providing capabilities to assess data and models, select appropriate features, and utilize relevant evaluation metrics.
  • Address various validation needs, including verifying data integrity, inspecting data distributions, validating data splits, evaluating model performance, and comparing different models. Utilizing tools like Deepchecks can assist in meeting these validation requirements effectively.

By improving model evaluation and testing practices in computer vision projects, we can ensure accurate assessments, robust generalization capabilities, and reliable deployment of models in real-world applications.

Pitfall 6: Neglecting model error analysis

Neglecting proper error analysis is a significant pitfall that can impede the success of computer vision projects. Error analysis involves identifying and understanding the mistakes made by the model, which is crucial for improving its performance and reliability. Failure to conduct a thorough error analysis can result in deploying models with persistent shortcomings that go unaddressed. In many cases, project teams solely focus on checking the model’s accuracy and celebrate if it meets the desired level. However, they often overlook the valuable insights that errors can provide about how the model functions.

For example, consider a computer vision system designed for autonomous driving to detect and avoid pedestrians. During the testing phase, the model consistently misclassifies certain objects, like bicycles, as pedestrians. Without conducting a comprehensive error analysis, the project team may not uncover the specific circumstances or patterns that cause these misclassifications. Consequently, the model would be deployed with this persistent error, potentially jeopardizing pedestrian safety.

To mitigate this pitfall, performing a detailed error analysis on the model’s predictions is crucial. There are several approaches to conducting error analysis:

  • Manual review: Randomly selecting data from various subsets and examining their predicted labels can help identify the number of incorrect predictions and note specific issues.
  • Correlation charts: Visualization techniques like correlation charts can display the number of correct and incorrect predictions, providing insights into the model’s performance.
  • Class activation maps (CAM): CAM helps data scientists understand the relevance of each image to the predicted class, aiding in error analysis and identifying potential weaknesses.
Thorough error analysis

Photo by Lukas

Thorough error analysis, including examining false positives and negatives, analyzing misclassified samples, and identifying patterns, helps teams understand the model’s mistakes. This understanding enables them to make appropriate adjustments like collecting diverse training data, refining feature extraction, or adjusting the model’s architecture. Robust evaluation, testing, and diligent error analysis ensure reliable and accurate computer vision models, leading to improved performance and user trust. When models underperform, conduct a thorough error analysis to identify root causes and enhance performance without panicking.

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10 Common Pitfalls When Building a Computer Vision Model

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Pitfall 7: Selecting an inappropriate transfer learning technique for your task

One common pitfall in computer vision projects is choosing an inappropriate transfer learning technique. Transfer learning in computer vision utilizes a pre-trained deep learning model to address a related new task, leveraging the knowledge gained from a large dataset and a complex model trained on a similar task. This approach improves learning efficiency and effectiveness by building upon existing knowledge. Transfer learning techniques can be broadly categorized into feature extraction and fine-tuning. While the choice depends on the specific problem, relying solely on feature extraction instead of fine-tuning the final layers often results in subpar model performance. Each transfer learning approach has its strengths and limitations, and using the wrong technique can negatively impact model performance and accuracy.

For instance, when classifying different types of animals, using a pre-trained model designed for object detection tasks may not be ideal. Object detection models focus on identifying bounding boxes and localizing objects, whereas animal classification primarily involves assigning labels. In such cases, selecting a pre-trained model specifically trained for animal classification tasks is more suitable.

To avoid this pitfall, carefully evaluate and select a transfer learning technique that aligns with your task’s requirements and characteristics. Consider factors such as the similarity between the source and target domains, the availability of pre-trained models trained on similar tasks, and the specific features and representations your task demands. By making the right transfer learning choice, you can effectively leverage the knowledge stored in pre-trained models and enhance the performance of your computer vision project. When uncertain, lean towards fine-tuning the final layers, particularly when working with limited image data.

Pitfall 8: Performing large-scale data augmentation on the CPU

Data augmentation is crucial in computer vision projects for enriching the training dataset with diverse examples. It involves applying transformations like rotations, translations, flips, and color distortions to generate additional training samples. However, a common pitfall to avoid is relying on the CPU for large-scale data augmentation instead of leveraging specialized hardware like GPUs or TPUs. Performing data augmentation on the CPU can be significantly slower and less efficient, mainly when dealing with large datasets or complex augmentation operations. The CPU lacks the parallel processing capabilities necessary for handling the computational demands of data augmentation, leading to longer training times and slower model iteration. Using the CPU for data augmentation may be feasible for smaller datasets, but it becomes impractical as the dataset size grows.

For example, consider a computer vision project that involves training a deep-learning model on a dataset of high-resolution images with extensive data augmentation. Employing CPU-based data augmentation would result in prolonged training times, hindering your ability to iterate and experiment with different models and hyperparameters efficiently.

To overcome this pitfall, it is advisable to utilize specialized hardware such as GPUs or TPUs for large-scale data augmentation. These hardware accelerators are designed for parallel processing tasks, enabling faster data augmentation and training times. You can streamline the data augmentation process by leveraging the appropriate hardware, leading to more efficient model training in your computer vision project. An example of a library that facilitates optimized data augmentation jobs is NVIDIA’s DALI, which allows for dataset loading directly onto the GPU, enhancing overall performance.

NVIDIA Data Loading Library (DALI) Illustration

NVIDIA Data Loading Library (DALI) Illustration | Source.

Pitfall 9: Thinking deployment is the final step

One common pitfall in computer vision projects is treating deployment as the final step and neglecting ongoing maintenance and improvement. While deployment is important, it’s essential to recognize that machine learning models require continuous monitoring and intervention to maintain optimal performance.

Computer vision models generalize based on a subset of data, and their accuracy can decline over time with new, unseen data. Additionally, models may struggle to adapt to complex user input and changing societal dynamics without human intervention. This highlights the need for continuous monitoring and refinement to address evolving needs and ensure the model remains effective.

Deploying a computer vision model should mark the beginning of a continuous improvement and maintenance phase, which includes:

  • Assigning roles and responsibilities to monitor the model’s performance using appropriate tools and alert systems.
  • Conducting root cause analysis to identify and address underlying issues.
  • Establishing key performance indicators (KPIs) and implementing a robust monitoring system.

Considering deployment as the final step is problematic because computer vision models require ongoing adaptation to changing data, user needs, and technological advancements. Continuous monitoring enables timely detection of issues like model drift, biases, or errors, facilitating proactive intervention and updates. Embracing deployment as an ongoing process fosters innovation, leveraging user feedback for enhancements and long-term value.

To avoid this pitfall, view deployment as a milestone in the larger lifecycle of your computer vision project. Establish processes for monitoring, gathering user feedback, and incorporating improvements based on real-world usage. By embracing continuous improvement and allocating resources for ongoing maintenance, you can ensure the long-term success of your deployed computer vision models.

Pitfall 10: Neglecting monitoring model usage/cost

When working on computer vision projects, it is crucial to focus not only on model performance but also on monitoring its usage and associated costs. Neglecting this aspect can lead to inefficient resource allocation, increased expenses, and underutilization of deployed models.

Similarly, overlooking cost monitoring can result in financial inefficiencies, particularly when dealing with resource-intensive computer vision models that require significant computational power and storage. Without careful monitoring, there is a risk of overspending on infrastructure or cloud services, especially for large-scale deployments or fluctuating demand.

For instance, in a healthcare application using computer vision for medical diagnosis, the failure to monitor model usage and costs can lead to overutilization, with the model unnecessarily invoked for every patient. This increases resource consumption and expenses. Conversely, underutilization can result in inefficient resource allocation and missed opportunities for value delivery.

To avoid this pitfall, it is crucial to actively monitor model usage and cost. This involves tracking metrics such as request volume, response times, and user interactions to understand usage patterns, effectiveness, popularity, and areas for improvement. It also helps identify issues like unexpected spikes, underutilization, or system bottlenecks. To mitigate this pitfall, establish monitoring mechanisms for both model usage and associated costs. Implement logging and analytics systems to gather relevant usage data, and incorporate cost monitoring and optimization strategies. By actively monitoring model usage and costs, you can ensure efficient resource allocation, optimize expenses, and make informed decisions.

Final Thoughts

Success in computer vision projects comes from a holistic approach, considering technical, organizational, and strategic aspects. Embrace proactive and adaptive mindsets to unlock the full potential of computer vision technology, driving innovation and creating opportunities across domains. With the right strategies, expertise, and dedication, computer vision projects deliver value and advance society.

In conclusion, be aware of and avoid these pitfalls in your model deployment process. Stay vigilant and continue doing great work. Check out our blog for more insights on Machine Learning topics to stay updated and navigate challenges. Join our community of data scientists and ML engineers for support and knowledge sharing when facing additional challenges or having questions. Take it one step at a time and focus on building amazing solutions.

Deepchecks For LLM VALIDATION

10 Common Pitfalls When Building a Computer Vision Model

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison
TRY LLM VALIDATION

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