Enhancing Your Amazon Rekognition Custom Labels Model

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In this article, we explore effective strategies to optimize the performance of your computer vision models utilizing Amazon Rekognition Custom Labels. This fully managed service allows you to create tailored computer vision models for applications like image classification and object detection. By leveraging pre-trained models from Amazon Rekognition—developed using millions of images across diverse categories—you can kickstart your project with merely a handful of training images, often just a few hundred, tailored to your specific needs. The service simplifies the model-building process by automatically analyzing your training data, selecting the appropriate machine learning algorithms, determining instance types, and training multiple candidate models with various hyperparameter configurations to produce the most effective trained model. The intuitive interface provided through the AWS Management Console facilitates the entire machine learning workflow, which includes image labeling, model training, deployment, and result visualization.

However, there may be instances when your model’s accuracy falls short, and options for configuration adjustments are limited. Several underlying factors significantly contribute to the development of a high-performing model, including:

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

To establish a production-grade Rekognition Custom Labels model, follow these general steps:

1. Define Your Taxonomy: Clearly outline the attributes or items you wish to identify in your images.
2. Gather Relevant Data: This is the most crucial step. Ensure that the images you collect accurately represent what you will encounter in a real-world environment. Incorporate images with varying backgrounds, lighting conditions, and angles. Create distinct training and testing datasets by splitting the collected images, ensuring that only real-world images are included in the testing dataset—synthetic images should be avoided. Proper annotations are vital for model performance; ensure bounding boxes are snug around the objects and labels are precise. For more insights on building an effective dataset, check out this blog post.
3. Analyze Training Metrics: Utilize the aforementioned datasets to train your model and evaluate training metrics such as F1 score, precision, and recall. We will delve deeper into analyzing these metrics later on.
4. Evaluate the Trained Model: Assess the predictions using a set of unseen images that have known labels. This evaluation step is essential to confirm that the model performs adequately in a production context.
5. Re-training (if necessary): Training machine learning models is typically an iterative process, and computer vision models are no exception. Review the results from Step 4 and determine if additional images should be incorporated into the training data, then repeat Steps 3-5.

Our focus in this article will be on best practices for collecting relevant data (Step 2) and evaluating your training metrics (Step 3) to enhance your model’s performance.

Collecting Relevant Data

This step is critical for developing a production-grade Rekognition Custom Labels model. Specifically, you will need two datasets: one for training and another for testing. The training data is essential for model training, and investing time in curating a suitable training set is necessary. Rekognition Custom Labels models are optimized for F1 score on the testing dataset, making it imperative to assemble a testing dataset that accurately reflects real-world scenarios.

• Number of Images: We recommend a minimum of 15-20 images per label. A higher volume of images showcasing various conditions will bolster model performance.
• Balanced Dataset: Strive for an equal number of samples for each label. Avoid significant disparities, such as having 1,000 images for one label and only 50 for another, as this creates an imbalanced dataset.
• Diverse Image Types: Include images in your training and testing datasets that mirror what you will encounter in real life. For example, if distinguishing between living rooms and bedrooms, ensure both furnished and empty images of each type are included.
• Varying Backgrounds: Incorporate images with different backgrounds. Natural contexts often yield better results than plain settings.
• Lighting Conditions: Include images captured under various lighting conditions to encompass all potential scenarios during inference.
• Angles: Capture images from multiple angles to help the model learn diverse object characteristics.

In cases where obtaining a variety of images proves challenging, consider generating synthetic images for your training dataset. For more common image augmentation techniques, refer to this authority on the topic.

• Negative Labels: Adding negative labels can enhance model accuracy; for instance, including a label that does not match any target labels helps the model distinguish characteristics that are not part of the identified class.
• Handling Label Confusion: Analyze test dataset results to identify patterns that may have been overlooked in training. Sometimes, visual inspection of images can reveal issues. If your model struggles to differentiate between labels, such as “backyard” and “patio,” consider adding more images to these categories and clearly defining each label to improve accuracy.

Through these methodologies, you can significantly uplift the performance of your Amazon Rekognition Custom Labels model. Additionally, for those looking to explore opportunities in fulfillment centers, this resource provides valuable information.