How to Label Images for Computer Vision Models

To create a properly representative and high-quality training dataset, any image labeling for AI powered computer vision models involves a series of steps. These include preprocessing and augmentation, data splitting for quality checks and test runs, iterative refinement, and more.

In order to “see” and understand the world, computer vision models need to be trained on massive amounts of visual data. However, this data needs to be accurately annotated and categorized so the AI model can understand what it represents.

In image labeling for AI training, annotators provide the essential structure that enables AI models to learn what’s within the picture by carefully adding descriptive tags (labels) to images. This is the core of all work done for any machine learning image annotation.

However, despite the availability of automated labeling systems, for AI training data labeling, human supervision and quality control remains necessary, especially in sensitive cases like medical image analytics.

In this article we walk you through the various phases involved in image labeling for computer vision models. We will also discuss image labeling techniques and tools.

5 step process used in image labeling for AI-powered computer vision models

The best ways to label images for AI adopt a blend of three distinct approaches: manual, semi-automated, and synthetic.

While techniques like image classification, object detection, and semantic segmentation form the basis of image annotation, high-quality image labeling also requires preprocessing of images, data augmentation, and quality checks.

Before starting to label the images, you will have to optimize the images and make them easier for the model to learn from.

1. Preparing to label images for AI

To begin the image labeling process for preparing training data, first we need to optimize the images to create primary image datasets fit to label.

Common processing techniques include resizing and cropping, normalization, color space conversion, noise reduction and contrast enhancement.

To help the model learn from a wider range of examples, you need data augmentation, which involves creating new image data from existing ones. The augmented images are labeled in the same way as the original images to increase the diversity and size of the labeled dataset.

These techniques standardize image data, making it easier to conduct precise labeling for more accurate and efficient AI models.

Even the best machine learning algorithms produce unreliable results if trained on poorly labeled data.

To achieve AI model accuracy, you have to ensure the highest quality of image labels. You do this through clear labeling guidelines, splitting data for training, validation, and testing, using quality control metrics, and refining image datasets through iterations.

2. Consistent guidelines for AI training data labeling

• Ensuring clarity: The instructions should leave no room for ambiguity. You have to define exactly what qualifies as an object within the project’s domain, how bounding boxes should be drawn – tightly around objects or with a small margin, and how to handle complex cases like partially visible objects.
• Consistency: You always need to establish standardized naming conventions and labeling hierarchies to maintain consistency in team output.
• Labeling every object of interest: Objects important to the model’s goal are fully encompassed, even if partially obscured. Creating tight bounding boxes helps in improving labeling precision.

3. Splitting image data for training, validation, and testing

After labeling a dataset, you divide it into three distinct sets for training, validation, and testing. This split helps ensure the model responds well to unseen data and avoids overfitting.

Here’s a breakdown of the common data splits and their purposes:

• Training set (Typically 70-80%): This is the largest portion of your data and helps the model to recognize patterns and relationships between the image features and their corresponding labels. While training, the model iteratively adjusts its internal parameters to minimize errors in its predictions.
• Validation set (Typically 10-20%): This is essentially a dress rehearsal for the final test, allowing us you to refine the model before deploying it in the real world. It is used to fine-tune the model’s hyperparameters, which control the model’s learning behavior.

  By evaluating the model’s performance on the validation set, we you can identify potential overfitting and adjust hyperparameters to improve generalization.

• Testing set (Typically 10-20%): This is a held-out set of data used to provide an unbiased assessment of the model’s final performance on unseen data. By evaluating the model’s accuracy on the testing set, you learn how the model has generalized and decide if it’s ready for real-world application.

4. Using data quality control metrics

For high quality image labelling, we need to prioritize annotation quality and use quality control metrics. For this, we ensure tight bounding boxes with high Intersection over Union (IoU) for accurate object localization. We aim for high precision to minimize false positives and high recall to catch all relevant objects.

To ensure consistent image data labeling, we use Inter-Annotator Agreement (IAA) metrics like Cohen’s Kappa (two labelers), Fleiss’ Kappa (more than two), or Krippendorff’s Alpha (versatile) to measure how much we agree on our annotations. This helps us identify and fix inconsistencies, leading to better model training and performance evaluation.

5. Refining image datasets through iteration

You begin with a smaller, well-defined set of images representing the core concepts. This allows you to quickly spot weaknesses in the initial labeling. This is repeated and as the model improves, you gradually expand the dataset’s complexity and diversity.

You target specific inadequacies in labeling, or the dataset directly based on the model behavior in real time. Using the insights to collect new images that specifically target the weaknesses identified by the model, you retrain the model on the updated and expanded image dataset.

You also use insights from model errors to inform data augmentation techniques (e.g., rotating, blurring, or adding noise to images). This helps the model become more resilient to variations it might encounter in real-world applications.

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How iterative refinement works in machine learning image annotation

Consider developing an image classifier to detect and distinguish between different types of road signs (speed limit, stop, yield, etc.).

The following loop is followed for image labeling in ML:

• Deployment: An initial version of the model, trained on a dataset of road sign images, is deployed in the autonomous vehicle’s testing system.
• Evaluation: During test drives, the system logs instances where the model misclassifies or fails to detect road signs, especially under challenging conditions (poor lighting, partial occlusion, etc.).
• Analysis: The errors are analyzed. Here are some potential problems the feedback loop might reveal – some images in the training set may be labeled with the wrong type of road sign, the labeling instructions don’t address how to handle partially obstructed signs (trees, other vehicles, etc.), the dataset lacks enough images of road signs with weather damage, fading, or unusual angles.
• Label Adjustment: The image labeling guidelines are refined with more emphasis on partially visible signs, and instructions for distinguishing between similar-looking signs.
• Data Collection & Expansion: New images focusing on the problematic scenarios identified are collected and labeled according to the revised guidelines.
• Retraining: The model is retrained on the expanded and refined dataset, improving its ability to handle real-world complexity.

By continuously closing this loop, the image classifier becomes more efficient making its perception system more reliable.

Image annotation techniques for training data

It is important to select the right image labeling technique that aligns with your project’s goals for successful machine learning outcomes. Factors like image complexity, desired output, and model type have to be considered.

Here’s a breakdown of the primary image labeling techniques used for creating AI training data:

1. Image classification

This involves assigning a single label to an entire image based on its dominant content. For example, a picture of a dog might be classified as “dog” or a landscape photo as “mountains.”

Applications: Organizing personal photo libraries, automatic sorting of product images for e-commerce, initial screening of medical images for broad categories.

2. Object detection

Object detection involves identifying and localizing multiple objects of interest within a single image. This requires drawing bounding boxes or polygons around each detected object and assigning it a corresponding label.

For example, multiple objects like “cars,” “pedestrians,” and “traffic lights” can be identified and labeled within a street scene image.

Applications: Self-driving cars (identifying obstacles, pedestrians, traffic signals and other vehicles), robotics (picking and placing objects), inventory management (counting and locating items on shelves), manufacturing (spotting defects on assembly lines).

3. Semantic segmentation

It involves labeling every pixel in an image with its corresponding class. This provides highly detailed, pixel-level precision while labeling.

For instance, outlining precise boundaries of roads, buildings, cars, vegetation, etc., in a satellite image.

Applications: Autonomous vehicles (understanding the exact layout of the environment), medical imaging (detecting the shape and size of tumors or other anomalies), land-use analysis in satellite imagery

4. Instance segmentation

Though similar to semantic segmentation, instance segmentation goes a step further by differentiating between individual instances of the same class.

For example, in an image from a busy street, this outlines cars with pixel-level precision also labeling each instance of the ‘car’ class separately, i.e., “car 1,” “car 2,” etc.

Applications: Crowd counting and tracking, precise object tracking in videos, medical applications where analyzing individual organs or cells is crucial

Image annotation types

While labeling images you use one or more types of image annotation as listed below:

• Bounding boxes: Drawing a rectangular box around a specific object within the image.
• Polygon annotation: Drawing complex, irregular shapes around objects that are more flexible than bounding boxes in some cases.
• Landmark/Keypoint detection: Marking specific points of interest on an object, such as facial features (eyes, nose) or joints on the human body.
• 3D cuboid labeling: Similar to bounding boxes, but for 3D environments, it is often used for point cloud data from LiDAR sensors.
• Lines and splines: Used to annotate things like roads, lanes, or object boundaries in a continuous line.

Hitech BPO achieved 100% accuracy in annotating image datasets of kitchen waste to train a Swiss company’s food waste assessment models. This involved image segmentation, annotation, auditing, and iterative review. Rigorous training and validation ensured recognition of diverse European food items. By automating data feeding, the client’s models gained real-time insights into food waste metrics.

Read full case study »

Choosing a tool for high quality image labeling

When choosing an image labeling tool for AI training data, you need to consider several key factors. These include:

• Annotation types: The tool must support the specific annotation types and image annotation techniques required, such as bounding boxes, polygons, key points, semantic segmentation, or 3D cuboids, as different AI tasks demand different annotation methods.
• Data types: You need to verify that the tool is compatible with diverse image formats (JPEG, PNG, TIFF, etc.) and can efficiently handle large datasets.
• Usability: The tools should have an intuitive interface with features like zooming, panning, and keyboard shortcuts to streamline the annotation process.
• Collaboration & data quality control: To ensure accurate labeling among teams, the tool should allow collaboration, assigning tasks, tracking progress, and maintaining consistency in annotations.
• Quality assurance: The tool should have features for data quality control, such as annotation validation, consensus mechanisms, and the ability to identify and correct errors.
• Scalability: You should choose a tool that can scale with the need to handle larger datasets and complex annotations.
• Security: Data security is extremely important. You need to ensure the tool has robust security measures to protect sensitive image data.

By carefully considering these factors and adhering to image labelling best practices, you can select an image labeling tool that helps to create high-quality AI training data efficiently, ultimately leading to better-performing machine learning models.

Roboflow Annotate, Labelbox, Scale AI, SuperAnnotate, and Dataloop are a few among the top image annotation tools available today.

Conclusion

To accurately label images for AI-powered models, you need to follow best practices, such as creating clear and concise annotation guidelines, training annotators thoroughly on project specifications, and incorporating review and consensus stages in the annotation workflow. It is important to remember that the quality of input data directly influences the performance of the AI model.

Moving toward a future where AI-powered communication becomes the norm, the reliability and performance of computer vision models will assume center stage in our everyday life, and the demand for accurately labeled image data will keep growing. Therefore, investing time and resources in proper image labeling and tie-ups with reliable image labeling services will hold the key to success in AI projects, now and in the foreseeable future.