How to Train AI Image Models Step By Step
Understanding AI image generation starts by grasping how AI models learn to recognise, categorise and interpret images, a fundamental aspect of training a custom Stable Diffusion model or any AI generation tool.
In essence, the quality and consistency of an AI image model depend on the depth of learning it gains during the training process, where the machine learning algorithm studies large datasets of annotated images to comprehend the multitude of factors that make a complete graphic.
For example, an AI image model will know the differences between colours, textures, shapes, humans, animals, backgrounds, buildings, and countless other variables and will draw on this knowledge to produce outputs that match your prompt.
Training Image Recognition AI Models
Training begins by creating an image database or collating datasets representing the breadth of the understanding you would like your AI model to possess. However, inputting huge amounts of data without it being properly prepared, categorised, labelled, and ordered is ineffective because the algorithm needs to know the context and purpose of each input piece of data to be able to utilise it properly.
Step 1: Preparing Data for AI Model Training
Data must be augmented to provide high-level training for an AI image model, which means you avoid overfitting and can create a sufficient volume of references to support the desired level of functionality. For example, you might need to input varied data and cross-check your datasets to ensure you have included the following:
- Image references with different orientations
- Full-colour, grayscale and black and white image data
- Data images with a variety of clarity or blurring
The better the diversity and quality of the datasets used, the more input data an image model has to analyse and reference in the future.
Next, your AI model needs to understand the classification of objects, colours, or any other element of a graphic, usually with at least 200 images as a basic minimum for a simple image detection model–but often with tens of thousands of input references necessary to create a suitably comprehensive bank of knowledge.
Annotating images helps the AI model to learn what the image shows, which images or features are important, and how each can vary. An example could include inputting varied pictures of a cat with different textures, coats, colours, sizes, and postures rather than expecting an AI model to be able to extrapolate any pose or breed from one input that is not representative of the multiple ways this one character could appear.
Step 2: Understanding Neural Networks
The practical training of your AI image model will vary with the network you are using. Still, the basis is that an algorithm does not interpret one image as a finished item but will unpick the elements of the data pixel by pixel.
Neural networks act similarly to human neurons, which extract features from the image, and then feed them into the network to be analysed, which means that the better the annotation accompanying your datasets, the better the algorithm will understand the relevant features and categorise them correctly.
The architecture behind the network comprises layers that perform different actions, such as applying a convolutional layer over all pixels within one image. The AI then has a map that sets out all the features and provides a logical way to analyse and log all the components within the image.
Each layer can focus on a different feature, such as textures, shapes, colours and the relationship of one element to the next to grasp scope, size, and dimension. Additional layers include:
- Activation layers to help the AI image model process results faster
- Pooling layers, which compress image data and clean it
- Flattening layers combine all the information and results to aid image recognition
Once all the training is complete and all layers have been applied, you can test the image model to see whether it can accurately analyse, identify, categorise, and store input data extracted from datasets.
Step 3: Image Model Validation
The third and final stage is to validate the AI image model to see whether it performs to your expectations and is suitable for integration into any wider system. Testing involves a new dataset, which evaluates how well the trained model performs–this should be an unknown dataset and verify whether the model can work correctly when analysing data it hasn't experienced before.
The results should be compared with those from the training in step two to see whether the model performs accurately and consistently. Where there are differences, a new training phase may be necessary, or the parameters introduced during training can be tweaked to ensure the model has been trained on sufficiently labelled, augmented, and varied data to provide the results you expect.