Como você pode avaliar efetivamente o desempenho do sistema de visão computacional em cenários do mundo real?

The first step in defining objectives and metrics is to identify what are you trying to achieve with your system? What are the most important factors for success? Once you have a good understanding of the problem domain and use case, you can start to define your objectives and metrics. Consider the following factors: Accuracy: How well does your system perform at its primary task? Precision: How often does your system correctly identify positive examples? Recall: How often does your system identify all positive examples? Speed: How quickly does your system perform its task? Robustness: How well does your system perform in different environments and conditions? Reliability: How often does your system produce the correct output?

Defining clear objectives and metrics is crucial in any computer vision project to ensure that you have a measurable and achievable goal. here are some tips: 1-Understand the Problem: Start by gaining a deep understanding of the problem you want to solve with computer vision. 2-Determine what you aim to achieve with your computer vision project. 3-Choose appropriate metrics that align with your objectives. Metrics should be specific, measurable, and relevant to the problem. Common metrics for computer vision projects include accuracy, precision, recall, F1 score, mean Average Precision 4-Determine how you'll annotate and label your data. Labeling is essential for supervised learning tasks in computer vision.

Evaluating real-world computer vision systems requires diverse datasets, task-specific metrics, noise and occlusion handling, and robustness testing. Using various metrics, benchmarking against state-of-the-art models, and gathering user feedback ensures comprehensive system assessment and enhancement.

To evaluate a computer vision system in real-world scenarios, use a combination of metrics like accuracy, precision, recall, and F1-score. Conduct testing in diverse, representative environments, and consider factors like lighting, weather, and object variability. Measure the system's robustness, latency, and scalability. Incorporate user feedback for subjective assessments. Continuous monitoring, retraining, and benchmarking against industry standards are essential for ongoing performance evaluation.

Choose a representative dataset that reflects real-world conditions and scenarios, unseen by the system during training. Here is an example of how to choose a representative dataset for a computer vision system designed to detect traffic signs: Diversity: Include images of different traffic signs, including common and uncommon signs, as well as signs from different countries and regions. Complexity: Include images of traffic signs in different lighting conditions, weather conditions, and backgrounds. Variability: Include images of traffic signs that are occluded, damaged, or partially obscured. By using a representative dataset, you can ensure that your computer vision system is evaluated fairly and accurately.

1-Data Size: Consider the amount of data you need. More data is generally better for training deep learning models, . 2-Data Diversity: Ensure that your dataset is diverse and representative of the real-world scenarios your model will encounter. Diversity can include variations in lighting. 3-Avoid bias in your dataset. Be conscious of any potential biases in the data, 4-Data Split: Divide your dataset into training, validation, and testing subsets. 5-Data Quality: Assess the quality of the data. Ensure that images are clear, labels are accurate.

Let's take an example: Problem: How to evaluate the performance of a computer vision system for detecting tumors in medical images? Solution: Use a combination of quantitative and qualitative evaluation methods. Quantitative metrics such as accuracy, precision, recall, and F1-scores can be used to measure the system's ability to detect tumours in a dataset of medical images. Qualitative metrics such as saliency maps and attention maps can be used to assess the system's ability to identify and localize tumours in medical images. By carefully selecting and applying appropriate evaluation methods, you can gain valuable insights into the performance of your computer vision system in real-world scenarios.

Evaluation of computer vision models is very necessary before deployment of models to solve real-world problems. It requires research within the specific domain, involving rigorous model testing and validation. "I maintain a consistent practice of generating documentation for each test, thus expediting the workflow when confronting analogous scenarios or novel use cases." Experimenting with models using real-world data can yield favorable and optimistic outcomes. Subsequently, you can deploy the optimized model to address real-world challenges.

Iterating and continuously improving your system in computer vision projects is a crucial part of the development process , here are some tips: 1-Gather Feedback: Collect feedback from stakeholders, domain experts, and end-users. Understand their needs, expectations 2-Analyze Performance Metrics: Review the performance metrics of your current system. Identify the weaknesses and areas where the model is falling short of your project's objectives. 3-Identify Weaknesses: Pinpoint specific weaknesses in your system. Determine whether the issues are related to the data, model architecture, hyperparameters, or other factors.

Back in 1996, when working with Entertainment Robot Company at Sony we started to look at computer vision as AIBO had a camera in its nose and we had to capture, understand and process the data, in order to deploy the system in diverse, representative environments and conditions. We monitored accuracy, speed, and robustness, comparing its outputs with ground truth data. We paid attention to factors like varying light conditions, object occlusions, and real-world anomalies. We gathered qualitative feedback from end-users to assess usability and reliability. Continuously iterated based on real-world results, ensuring the system is both technically sound and practically applicable.

Beyond traditional evaluation methods and standard metrics, ensuring the readiness of your model for production requires live testing. In this context, it's crucial to assess your model's performance in a real-world environment, where metrics like user engagement and system latency take center stage. The challenge lies in the inability to instantly update models in production, as a poorly performing model could pose a significant risk to the entire business. To address this, various methods such as shadow deployment, AB testing, or Canary deployment are employed to ensure reliable real-world testing.