Computer Vision (CV) is a scientific field that researches software systems trained to extract information from visual data, analyze it, and draw conclusions based on the analysis. The area consists of so-called CV or vision AI tasks. Each task is unique and incorporates techniques and heuristics for acquiring, processing, analyzing, understanding the data, and extracting various details from it. On this page, we will:
Let’s jump in.
As the name suggests, the Attribute Prediction task is concerned with detecting the attributes of the objects in the image.
Visual attributes contain essential information about the objects and the scene overall. One object may possess several attributes, for example, color, material, geometric properties (size, shape), position in space, state (jumping, moving, laying), and many more.
Attribute Prediction can also be referred to as a Multi-Label Classification problem as it also focuses on predicting all the relevant attributes of a given object.
Attribute Prediction (AP) is a Classification task that allows you to predict one or more labels related to the object. This means you can assign multiple attributes to the same object in the training data. The image below shows how an example of input and output can look for the AP task.
The general Attribute Prediction algorithm in ML is as follows:
In general, Attribute Prediction can be applied in the following cases:
As we have established earlier, the Attribute Prediction task may interchangeably be called the Multi-Label Classification task. Such a name is frequently used by researchers in academic papers and often confuses those unfamiliar with the topic.
Another common CV technique is Multi-Class Classification. Even though these two sound similar, they refer to different tasks.
We must mention that only a few datasets are devoted to Attribute Prediction exclusively. This might be partially explained by the fact that objects in the image can be described in various ways. The names and the choices of the attributes might depend on the annotator’s perspective or linguistic preference (for example, one annotator could describe someone’s eye color as blue and another - as light grey). Thus, providing exhaustive and uniform annotations to each object is a large-scale task.
Nevertheless, some datasets explore the object attributes in depth. They include:
You can also check out the Multi-Label CLassification benchmarks of other datasets. We will provide some examples of the datasets and SOTA (state-of-the-art models benchmarked against these datasets) below.
Data annotation might be a bottleneck for AI startups as the conventional labeling approach is both costly and time-consuming. Hasty’s data-centric ML platform addresses the pain and automates 90% of the work needed to build and optimize your dataset for the most advanced use cases with our self-learning assistants using AI to train AI.
The primary focus of Hasty is the vision AI field. To streamline your Attribute Prediction annotation experience, Hasty offers an AI-powered Label Attribute assistant that predicts the image attributes automatically. Please visit our documentation to learn how to create attributes in detail.
When it comes to model building, Hasty’s Model Playground supports many modern neural network architectures. For Attribute Prediction, these are:
As for the Machine Learning metrics for the Attribute Prediction case, Hasty implements:
As of today, these are the key options Hasty has for the Attribute Prediction cases. If you would like a more detailed overview, please check out the further resources or book a demo to get deeper into Hasty with our help.
Only 13% of vision AI projects make it to production, with Hasty we boost that number to 100%.
Start for free Request a demo