Managing a computer vision project is not easy. There's lots of moving factors that you need to evaluate before you deploy it onto production. For instance:

1. Managing your dataset correctly in training and testing.
2. Preprocessing your data properly
3. Selecting the right model
4. Performing the right fine-tuning and deployment steps.

That's not all. The list goes on, and there's a lot of things that can go wrong when you're building a computer vision project.

In fact, it's reported by Gartner that only 53% of projects move into production due to issues with model training and refinement.

So, that's almost more than half the projects that actually end up failing due to poor implementation strategies.

So, let's explore some of the common pitfalls of building computer vision projects and understand how you can mitigate it to make sure you don't end up in that 53%.

But first, let's understand what exactly a computer vision project is.

What is a Computer Vision Project?

Computer vision is a subfield of artificial intelligence (AI) that teaches computers to see, observe, and interpret visual cues in the world.

Using various algorithms and tools, a computer vision model can extract valuable information and make decisions by analyzing digital content like images and videos.

For example, it's possible to create a computer vision solution that processes thousands of images of a specific product, identifying subtle defects unnoticeable to the human eye within minutes.

Computer vision solutions are applied in various fields like healthcare, autonomous vehicles, surveillance, and augmented reality, with each project tailored to meet specific needs and challenges.

The Process of a Computer Vision Project

The process of a computer vision project typically involves:

1. Defining and understanding the task; whether it's object detection, segmentation, classification, etc.
2. Collecting and preprocessing enough data for training, validating, and testing the computer vision model.
3. Selecting the appropriate model architecture and performance metrics for the task at hand.
4. Training the model and validating its performance.
5. Deploying, monitoring, and maintaining the project.

Whether you are an expert computer vision engineer or a novice, unforeseen challenges often emerge during the seemingly straightforward steps outlined above.

Let's explore some of the common pitfalls that can arise in a computer vision project and solutions to overcome them in the following sections.

TL;DR:

Common Pitfalls/Mistakes in Computer Vision Projects

1. Data Leakage

Ever found yourself in a situation where development phase performance metrics are off the charts, while there is a 10-20% drop in performance when applying your model to a real-world use case? Chances are, your computer vision project is subjected to data leakage.

Data leakage in computer vision tasks occurs when the data used for training contains unexpected additional information about the subject being estimated.

Common Mistakes:

Leakage can happen in two main ways:

1. Unintended features added to the training set: Including information like unique identifiers (file names, image resolutions), timestamps, or target labels in the training data allows the model to learn patterns associated with unseen data that would not be available during the inference stage.
2. Training set contamination with validation/test data: During the training phase, we typically use a validation set to update the model's weights and a test set to evaluate its generalizability on unseen data. If these sets are unintentionally mixed with the training data, the model may yield falsely optimistic results that cannot be replicated when applied to real-world data.

If we classify patients as either cancerous or non-cancerous using image data, including the images of the same patient in both the training and testing sets can result in performance metrics that are artificially boosted. The model might acquire features related to a particular individual during training, achieving misleadingly high testing performance, sometimes even surpassing the training results by a significant margin.

2. Incompatibility between the Data & Model Domains

Selecting a model architecture without considering the compatibility between the data and model domains can lead to overfitting, underfitting, inefficiencies, and incompatibilities.

Common Mistakes

• Model complexity-data volume mismatch: When selecting a model for projects with limited data, such as in rare disease classification, it's essential to find a balance between the complexity of the model and the amount of available data. Complex models such as vision transformers (ViTs) need a large amount of data to train properly without overfitting.
• Model-task requirement misalignment: Choosing a model unsuitable for the task, whether it's classification, object detection, or facial recognition, can lead to inefficiencies. For instance, using an object detection model trained to generate bounding boxes for localization is inappropriate for image classification requiring label assignments.
• Model's objective-data features mismatch: For example, an architecture optimized for large object detection may not be suitable if the actual use case involves detecting smaller objects.
• Pre-trained model misuse: While utilizing pre-trained models can accelerate development efforts, managers must recognize the potential for inheriting biases from the model's original training data. Such biases may result in inaccuracies, such as false positives, within your application.

3. Inconsistent Labeling & Augmentation Process

Building supervised computer vision models requires well-annotated data, often expanded through augmentation.

Common Mistakes:

However, some of the computer vision challenges arising during labeling and augmentation are:

• Inconsistent labeling: Annotation and labeling typically involve multiple domain experts. Collaboration among experts without clear guidelines leads to labeling variations. This inconsistency results in errors, impacting the model's performance negatively.
• Imbalanced and limited diversity in datasets: A dataset's effectiveness depends on its representation of all output classes or features. Imbalances, where one feature dominates will reduce the model's performance on other classes.
• Issues in data augmentation: Excessive augmentations such as too many rotations or scaling, may distort images, destroy original information, and introduce unnatural instances that amplify biases present in the initial data.
• Overfitting to augmented data: There's a risk of the model overfitting to specific augmentations, limiting its ability to generalize to new, unseen data and compromising overall effectiveness.

4. Selecting the wrong fine-tuning or domain adaptation technique

Domain adaptation is a form of transfer learning, where computer vision models adapt to new datasets while maintaining the same feature space. This is especially valuable for enhancing model performance in target domains with insufficient data by obtaining knowledge gained from a related domain with adequate data.

Common Mistakes

Some of the factors that can lead to performance challenges are:

• Dissimilarity of tasks: The source and target task dissimilarity will lead to reduced performance. For instance, applying a network trained on Imagenet to medical imaging, specifically chest X-rays, may yield weak results due to the dissimilarity between the tasks. Ensuring similarity between the source and target tasks is crucial for effective domain adaptation.
• Incorrect dataset size and distribution: During model fine-tuning, even with a smaller dataset, if the dataset does not cover the diversity of data encountered during model inference, the model will perform poorly at the inference stage.
• Feature extraction vs. fine-tuning: When adapting pre-existing models to new tasks through transfer learning, there are primarily two approaches: feature extraction and fine-tuning. Feature extraction involves directly applying the model's existing knowledge with minimal alterations while fine-tuning entails further training the model on new data to refine its knowledge. Blindly favoring one technique over the other, such as solely relying on feature extraction without adjusting the final layers,  can lead to suboptimal model performance.

5. Improper Evaluation & Error Analysis

Lack of proper evaluation and testing can have an adverse impact on the reliability and effectiveness of computer vision projects. Without thorough assessment, models may face inaccuracies in performance, struggle with generalization, and risk deploying flawed models in real-world scenarios.

Common mistakes

• Reliance on training data metrics: Relying solely on metrics from the training dataset can give a false sense of model effectiveness. High accuracy on training data doesn't guarantee strong generalization. Rigorous evaluation on separate validation and test sets, especially in medical screening models, is crucial to avoid incorrect diagnoses.
• One-size-fits-all metrics: Using universal performance metrics may not be suitable for all tasks. Some metrics can be influenced by outliers, noise, or imbalanced data. For example, accuracy, while intuitive, can be misleading in tasks with skewed class distributions or multiple labels.
• Inadequate periodic evaluation: Although the model might exhibit satisfactory performance with current data, neglecting continuous periodic evaluation on new data could result in diminished long-term effectiveness. Over time, patterns may evolve, potentially compromising the model's reliability and its adaptability to changing data patterns.

6. Ignoring product limitations impacting deployment and model choice

Overlooking specific product limitations during deployment and model selection can lead to fatal mistakes in computer vision projects. Rushing into model implementation without a thorough requirement analysis and feasibility study is a serious error, with consequences that may only surface during deployment or monitoring, making corrective measures both challenging and costly.

Common Mistakes

Some of the common pitfalls are:

• Neglecting hardware and software constraints: Failing to consider the intended deployment platform, software dependencies, and network latencies may lead to impractical real-time performance. For instance, deploying a resource-intensive model designed for a cloud platform onto an edge device with limited processing power can cause significant slowdowns.
• Overlooking ethical considerations: Ignoring ethical concerns such as privacy, consent, transparency, accountability, fairness, bias, discrimination, or harm can have significant social, legal, or moral implications. For example, a computer vision system using facial recognition for identifying criminals may violate the privacy of innocent individuals or discriminate based on race, gender, or age.

How to Overcome These Mistakes?

1. Data Leakage

Some of the steps you can take to mitigate data leakage are:

• Thorough data preprocessing: Exclude features with potential leakages, like metadata, timestamps, or information not available during inference. Preprocess data to mirror real-world deployment conditions.
• Utilization of existing libraries: Utilize package tools like sci-kit-learn in Python to effortlessly apply distinct data preparation steps for various datasets, particularly in cross-validation, preventing data leakage between folds.
• Thorough validation procedures: Evaluate model performance on unseen data during validation, resembling real-world distribution. If performance exceeds expectations, investigate for potential data leakage.

2. Incompatibility between the Data & Model Domains

Choosing the appropriate model depends on your data's complexity, size, and nature. To address mismatches between data and model, consider:

• Model selection based on data quality and quantity: For computer vision tasks, architectures like Convolutional Neural Networks (CNNs), generative adversarial networks (GANs), or vision transformers (ViTs) are common. Advanced options like ResNet, Inception, or EfficientNet may enhance performance for specific applications. Managers should collaborate with their team to evaluate whether the existing data is adequate or if simpler models would be better suited to prevent potential overfitting issues.
• Utilizing existing models and frameworks: Incorporate pre-trained or customizable models from existing frameworks like TensorFlow or PyTorch, often integrated with AutoML tools. These tools streamline model selection, hyperparameter tuning, and overall performance optimization.
• Avoid misusing pre-trained models: It's essential to conduct thorough testing across diverse datasets and consult with domain experts to identify and correct any biases existing in pre-trained models. Implementing regular reviews and updates to the model based on these findings can help mitigate this risk.

3. Inconsistent Labeling & Augmentation Process

Solutions to issues arising due to inconsistent labeling and augmentation processes include:

• Standardize the labeling procedure:  Establish comprehensive guidelines for annotators, conduct regular reviews, and use annotation tools like DagsHub annotations for reproducible, scalable, and version-controlled labeling, ensuring standardized practices across annotators.
• Proper augmentation to resolve data imbalance: Apply targeted data augmentation to minority classes for balanced dataset representation. Implement stratified sampling during dataset splitting for consistent class distribution in training, validation, and test sets.
• Optimal augmentation techniques: Carefully select and apply augmentation techniques, avoiding excessive rotations or scaling that may distort images and introduce unnatural instances.
• Regularization to prevent overfitting to augmented data: Employ diverse augmentation strategies and apply regularization techniques like dropout or weight decay to prevent overfitting to specific transformations. Evaluate model performance on non-augmented data for generalization.

4. Selecting the wrong fine-tuning or domain adaptation technique

To avoid pitfalls related to fine-tuning and domain adaptation:

• Careful selection of domain adaptation technique: Choose a transfer learning technique aligned with task requirements, considering domain similarity, availability of pre-trained models, and specific task features. Opting for the right transfer learning approach enhances performance, particularly when dealing with limited image data.
• Feature extraction vs. fine-tuning consideration: Deciding between these approaches depends on the similarity of the new task to the model's original training and the amount of new data available. Understanding the trade-offs and choosing the best approach for the specific project goals is critical. When uncertain, prioritize fine-tuning the final layers of pre-trained models, particularly when working with limited image data and the number of target classes is different for your use case. Consider fine-tuning all layers of the model when the new task is significantly different from pre-training, requiring the model to learn new features across all abstraction levels.
• Thorough evaluation: Benchmark different models and fine-tune parameters through rigorous evaluation, ensuring alignment with project requirements. Tweak hyperparameters like learning rate and batch size to optimize training, enhancing overall model performance. Utilize tools like DagsHub that integrate MLFlow tracking for efficient management of model versions with varying hyperparameters.

5. Improper Evaluation & Error Analysis

For effective model evaluation and error analysis, consider these steps:

• Select evaluation metrics based on the algorithm: Research and identify specific evaluation metrics tailored to the applied algorithm. Use a range of relevant metrics, such as precision, recall, F1-score, ROC curve, AUC, IoU, or mAP, based on the task and data for a comprehensive understanding of the model's strengths and weaknesses.
• Appropriate data partitioning: Partition the data into training, validation, and testing sets. Evaluate the model on the validation set during training for informed decisions on hyperparameters and model selection.
• Test on unseen data: Assess the model's performance on unseen test data, ensuring it meets desired metrics.
• Utilize efficient tools: Leverage libraries and tools designed for streamlined model evaluation to enhance efficiency.
• Periodic evaluation: Establishing a routine for periodic evaluation against new data can further validate the model's long-term reliability and adaptability to changes in data patterns.

6. Ignoring product limitations impacting deployment and model choice

The challenges encountered due to product limitations can be mitigated by:

• Reframe the problem statement: Establishing precise requirements and product metrics. Visualize the product creation stages to ensure team alignment, iterating and narrowing the scope for clarity and purpose.
• Employ an effective project management framework: Tools like DagsHub can be used for a high-level view and version-specific representation of the data pipeline. Utilize it to understand the project, review updates, or onboard new team members without having inconsistencies.
• Model deployment considerations: Consider hardware constraints, storage limitations, and real-time model requirements. For instance, for smartphone deployment, focus on models like MobileNets and ShuffleNets that balance computational complexity, size, and high-performance accuracy.
• Address legal and ethical considerations: To proactively address ethical considerations, managers should establish a framework for ethical review. This process should involve conducting privacy impact assessments, establishing clear consent procedures, and conducting fairness audits at various stages of model development. Consulting with various stakeholders, such as legal advisors and potentially affected communities, can offer different viewpoints and uncover unforeseen ethical challenges. By adopting such a framework, organizations not only reduce legal and moral risks but also foster trust in the technology among users and the wider community.

Conclusion

Navigating a successful computer vision project requires careful consideration of pitfalls such as data leakage, domain mismatches, labeling inconsistencies, improper evaluation, and overlooking product limitations. By addressing these computer vision challenges with tailored solutions, developers can enhance model robustness, ensuring effective deployment and successful computer vision products.