6 Steps to Building Training Datasets for Computer Vision Models (2023)

Computer vision (CV) technology is advancing rapidly, with revolutionary applications in the following sectors:

• Healthcare
• Retail
• Automotive
• Manufacturing
• Agriculture

As the demand for computer vision-enabled systems grows, so does the demand for well-trained computer vision models. To produce high-quality results, these models require large amounts of training data, which must be of high quality and must be accurately labeled. However, collecting such datasets can be challenging due to the costs involved in the collection process and the time it consumes.

This article explores the 6 essential steps for collecting and building training datasets specifically for computer vision models to help developers and business leaders to train and implement robust computer vision models in their businesses.

1. Understanding your data requirements

It’s crucial to know the kind of data your particular computer vision model requires before you start gathering it. Some factors to consider include:

The type of computer vision model you’re developing

The type of computer vision model can vary from project to project. You can choose from the following:

• Image segmentation: This type of system involves breaking down an image into its main components, such as shapes and objects. Image segmentation systems are useful for tasks such as classifying different objects in an image or finding features within the image.
• Image classification: Image classification is designed to take an image or set of images and classify them into a predetermined set of categories.
• Object detection: This computer vision system is designed to detect objects in an image. It can be used to identify faces, cars, or other items of interest.
• Facial recognition: A facial recognition system is developed to recognize and identify faces from images. It is commonly used in security systems, as it can be used to detect intruders or unauthorized individuals accessing a facility.
• Edge detection: Edge detection computer vision systems are used to identify the boundaries of objects in an image. This type of system is useful for tasks such as identifying roads, sidewalks, or other features in an image.
• Pattern detection: These types of systems are designed to identify patterns or specific features in an image. These systems are commonly used for tasks such as recognizing text in an image or detecting a specific color.

The kind of training images or videos you’ll use

This involves the type of visual data that will be used. For instance, a quality inspection system for car parts can not be trained with image datasets of food. Data types can be:

• Images of faces for a facial recognition system
• Images of roads/streets for a self-driving system
• Videos of people walking on a street for a surveillance system, etc.

The kind of object (or objects) you’re aiming for your model to detect

You need to consider what kind of objects your computer vision system needs to detect. If a system needs to detect pedestrians, then it will require image or video datasets of people walking on sidewalks or while crossing the road.

The environment in which your model will be operated

This is taken into account to ensure that the system will work well in real-world circumstances. This is due to the fact that environmental factors like lighting, background clutter, and object occlusion can significantly affect how well a computer vision system performs.

The system can be trained to recognize objects and features under comparable circumstances and more effectively deal with difficulties like changes in lighting and background clutter. This is done by collecting training data that properly simulate the environment in which the system will be used.

2. Selecting the right data collection method

For a computer vision system, the method used to collect the data is crucial because it has a direct impact on the quantity, quality, and variability of the whole dataset. Making sure that the computer vision system can learn from a variety of representative data is essential for producing accurate predictions and results. You can choose from the following methods:

1. Custom crowdsourcing: Crowdsourcing is an effective method for collecting large and diverse image or video datasets in a limited period of time.
2. Private collection: In-house data collection, which is relatively expensive but offers highly personalized datasets.
3. Precleaned and prepackaged data: Readily available datasets which are much cheaper than other methods but offer a limited level of quality.
4. Automated data collection: The quickest method of collecting large-scale secondary online images and videos to create training datasets.

3. Preparing high-quality data

One of the most important steps in building training data for computer vision models is collecting high-quality data. This includes ensuring that the images and videos you collect are:

Diversity

To increase the robustness of the model to variations in the real-world environment, make sure the data collection contains a diverse range of objects, positions, lighting settings, and backgrounds.

Sponsored

Diverse computer vision training datasets can be costly and take a long time to gather. Clickworker offers image and video datasets to train computer vision models through a crowdsourcing platform. Their global team of over 4.5 million workers offers scalable and diverse data collection and image annotation services.

Annotation quality

To clearly and precisely recognize the location and class of objects in the photos or videos, the data should be annotated with accurate labels, bounding boxes, or masks.

Comprehensive

The data gathered should accurately represent the environment in which the system will function, with a focus on the specific objects and features that are relevant to the project.

Balanced

Make sure that the dataset is balanced, with a similar number of images or videos for each class of object, to avoid biases in the model towards certain classes.

Quality of images/videos

The images and videos should have good resolution and be free from distortions such as blur, noise, and compression that could negatively impact the performance of the model. You need to also make sure that the images are authentic and not altered through digital software such as photoshop.

To learn more about how to ensure quality while collecting training data, check out this article.

4. Labeling your data

Data annotation or labeling provides the computer vision system with labeled and readable examples to learn from, allowing it to accurately predict new, unseen data. You can consider the following factors:

Annotation guidelines

Create precise and concise annotation guidelines that outline the data to be labeled, how to label it, and examples to aid annotators in understanding the optimal results. Additionally, make sure the object classes and attributes that need to be annotated are clearly defined and that all of the annotators are on the same page.

Annotator quality

Choose annotators who have experience in the relevant fields/domains, and keep an eye on performance. For instance, not anyone can perform medical image annotation, they require a specific level of experience.

Annotation tools

Select tools that are compatible with the desired annotation format and facilitate efficient annotation. Leverage automated data labeling if necessary.

Quality control

Implement routine quality checks to keep an eye on annotator performance, ensure consistent annotations of each data point, and spot and correct errors.

Leverage the human-in-the-loop approach: Use a combination of human annotators and automated tools to get the best results.

You can check out the following articles to learn more data annotation for specific data types:

• Quick Guide to Video Annotation Tools and Types
• Video Annotation: In-depth guide and Use Cases

5. Augmenting your data

Data augmentation is the process of creating new training data by manipulating existing image data. This can include techniques such as:

• Rotating, flipping, and cropping images
• Adding noise or blur to images
• Using color or brightness adjustments

The goal is to artificially increase the size of the training dataset and reduce overfitting, which occurs when a model is too closely fit to the training data, leading to poor generalization to new data.

Here is an example:

6. Validating and testing

To make sure your data will be useful for training your computer vision model, validate and test it after you’ve collected and labeled it. This can be done through techniques such as:

• Split your data into training and validation sets
• Use cross-validation to ensure your data is representative
• Test your computer vision model on real-world data to ensure it generalizes well

This is done to ensure the model is correctly learning and to avoid AI overfitting. The performance of the model is measured on a different, unknown data set during training. Validation and testing both contribute to ensuring the model’s quality and its capacity to generalize to new data.

To learn more about training data collection for any AI/ML solution, download our free whitepaper:

Get Data Collection Whitepaper

Further reading

• Top 4 Facial Recognition Data Collection Methods

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