Enhancing Your Amazon Rekognition Custom Labels Model: Best Practices

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In this article, we explore effective strategies to enhance the performance of your computer vision models using Amazon Rekognition Custom Labels. This fully managed service simplifies the process of building tailored computer vision models for tasks such as image classification and object detection. Rekognition Custom Labels leverages pre-trained models that have been trained on millions of images across various categories, allowing you to start with a smaller, specific set of training images—often just a few hundred. This service alleviates the complexities of custom model development by automatically inspecting training data, selecting appropriate machine learning algorithms, choosing instance types, training multiple candidate models with different hyperparameter configurations, and ultimately presenting the best-performing model. Additionally, it offers a user-friendly interface through the AWS Management Console for managing the entire machine learning workflow, from labeling images to deploying the model and visualizing results.

However, there may be instances when your model’s accuracy falls short, leaving you with limited options to adjust its settings. Several underlying factors significantly influence the creation of a high-performing model, including:

• Image angle
• Resolution
• Aspect ratio
• Light exposure
• Clarity and richness of the background
• Color contrast
• Sample data size

Steps to Train a Production-Ready Rekognition Custom Labels Model

1. Define Your Taxonomy: Establish the list of attributes or items you wish to identify within your images.
2. Gather Relevant Data: This is the most critical step, where you need to collect images that accurately reflect what you would encounter in a real-world environment. This may include variations in backgrounds, lighting, and camera angles. Create your training and testing datasets by splitting these images. Ensure that your testing dataset consists only of real-world images, excluding any synthetic ones. Proper annotations are vital for model performance; make sure bounding boxes are tightly fitted around objects and that labels are precise. For additional insights into building a suitable dataset, check out this blog post here.
3. Review Training Metrics: Utilize the datasets for training your model and analyze the training metrics such as F1 score, precision, and recall. We will delve deeper into how to assess these metrics later in this article.
4. Evaluate the Model: Use a set of unseen images (not part of the training data) with known labels to test the model’s predictions. This step is essential to ensure that the model meets expectations in a production context.
5. Retraining (if necessary): Training a machine learning model is often an iterative process. Based on your evaluation in Step 4, determine if additional images are needed for the training data, and repeat Steps 3 to 5 as necessary.

In this discussion, we will primarily focus on best practices for gathering relevant data (Step 2) and evaluating your training metrics (Step 3) to refine your model’s performance.

Collecting Relevant Data

This is the pivotal stage in training a production-grade Rekognition Custom Labels model. You will be working with two datasets: training and testing. The training dataset is crucial for model development, and careful attention must be paid to its composition. Rekognition Custom Labels models are optimized for F1 scores on the testing dataset to identify the most accurate model for your project. Therefore, curating a testing dataset that mirrors real-world scenarios is vital.

• Image Quantity: Aim for a minimum of 15-20 images per label. More images that offer diverse variations reflecting your use case will enhance model performance.
• Balanced Dataset: Ideally, each label should have a comparable number of samples. Avoid significant disparities in the number of images per label. A dataset where one label has 1,000 images while another has only 50 indicates an imbalanced dataset. Aim to prevent a lopsided ratio of 1:50 between the label with the least images and the one with the most.
• Diverse Image Types: Include images in both the training and testing datasets that represent what you will encounter in practical scenarios. For instance, if classifying images of living rooms versus bedrooms, gather both furnished and unfurnished images of each.
• Different Backgrounds: Incorporate images with varied backgrounds. Contextual images yield superior results compared to plain backgrounds.
• Varying Lighting Conditions: Include images under different lighting conditions to encompass the various scenarios that may arise during inference, such as images taken with and without flash. Consider including images with differing saturation, hue, and brightness levels.
• Different Angles: Capture images of objects from various angles to help the model learn their distinct features.
• Incorporate Negative Labels: For image classification tasks, adding negative labels can bolster model accuracy. For instance, introducing a negative label that does not match any required labels helps the model learn to distinguish characteristics not associated with the desired labels.
• Address Label Confusion: Analyze results from the test dataset to identify patterns that may have been overlooked in either the training or testing datasets. Often, visually inspecting the images can reveal such patterns. If the model struggles to differentiate between labels, such as backyard versus patio, adding more images to these categories and clarifying labels can enhance model accuracy.

In conclusion, following these best practices can significantly improve the performance of your Amazon Rekognition Custom Labels model. For further reading on onboarding new hires during challenging times, visit this excellent resource here.