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Discover effective strategies for enhancing your `CoreML` image classifier models through `object detection`, even with limited data.
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How to Refine a CoreML Image Classifier Model Using Object Detection Techniques

In the realm of machine learning, particularly within the sphere of computer vision, classifying images effectively can often present challenges, especially when dealing with overlapping classes or prioritizing certain labels over others. If you've created an image classifier model using CreateML and find yourself struggling with suboptimal results due to your current data labeling, you're not alone. This guide explores a solution that leverages existing data to enhance your classifier using an object detection model.

Understanding the Problem

You may have a sizable dataset of hundreds of thousands of carefully classified images, where each image contains one or more objects with defined labels. However, the way the labels are prioritized can lead to confusion and inefficiencies in the classification process. For instance:

Label a is assigned when Object A is present.

Label b is meant for Object B.

Label c is used when Object C appears.

Overlapping scenarios lead to singular labels when multiple objects exist, creating classification challenges.

This prioritization of objects means that if Object A and Object B are present, the model will only recognize Object A due to its higher priority. This is where the approach of utilizing an object detection model becomes particularly valuable.

The Proposed Solution: Multi-label Classification

Despite the issues of labeling, we can employ a strategy called multi-label classification where the model predicts probabilities for each object independently. A few key points include:

Independent Labeling: Multi-label classification allows the model to assess and predict the likelihood of the presence of Objects A, B, and C independently, providing a more nuanced understanding of the image composition.

Capabilities of CoreML: Unfortunately, it is essential to note that CreateML does not currently support the training of multi-label classification models directly, which poses a challenge.

Leveraging Existing Data

Given the constraints and concerns regarding building a new dataset for an object detection model from scratch, the question arises: Can we utilize our existing data effectively? Here are a few strategies to consider:

1. Integrate Object Detection with Your Current Set

Even with a smaller object detection dataset, you can:

Select Key Images: Identify and select key images from your existing dataset that align well with the specific objects you want to detect.

Train an Object Detection Model: Using these key images, train your object detection model separately, focusing specifically on the images that demonstrate clear examples of each object.

2. Augmentation Strategies

Data Augmentation: To enhance your smaller object detection dataset, consider applying data augmentation techniques such as flipping, rotation, and scaling to artificially increase the diversity of your dataset.

Transfer Learning: Employ models pretrained on similar tasks and fine-tune them using your specific dataset, which will often yield better results than training from scratch.

3. Use Probabilities to Guide Decisions

Instead of an all-or-nothing classification, use the probabilities generated from your object detection model to inform the classification process:

Threshold Settings: Set thresholds that indicate the presence of an object, allowing for a more nuanced classification based on probability outputs.

Conclusion: A Dual Approach

In summary, while it may seem daunting to refine a CoreML image classifier model when existing labeling complicates direct classification, employing the strategy of multi-label classification and integrating object