Every single piece of information on the web is annotated or labeled in some way or another. This is how we are able to tell images apart and search for objects in the first place. So image annotation or labeling is this process of tagging images for the purpose of evoking them as well as training machine learning.
This article goes through some of the ways DataEntryOutsourced can help with the possible solutions for image labeling.
What are the uses of integrating image labeling for eCommerce?
We know that labeling is essential for the purpose of being able to tell images on the internet and train artificial intelligence. This is how data is fed to machine learning models in the form of scraping.
In the arena of e-commerce, this can be implemented for displaying products accurately and letting the users know what it’s going to look like when they actually use it.
For instance, you could have a makeup brand using augmented reality to help users visualize how certain products are going to look on their faces. Several apparel, spectacles, and household item sellers are already using this technology to help users figure out what the object that they purchase is going to appear like in person in the teacher’s own space.
It also makes for a more personalized user experience, where the image labeling works to place things in your reality.
What are some of the challenges you can face during image annotation for ecommerce?
And there are quite a few ethical problems that come along with image annotation which need more transparent laws and regulations. For instance:
- Different annotators can often label the same image differently, resulting in incoherent or inconsistent annotations.
- Security and privacy are major concerns when it comes to labeling image annotation generative machine learning. This is because this process needs a steady supply of data that is often sourced from authorized as well as unauthorized places.
- Hallucinations are another major issue when it comes to annotations and labeling in generative machine learning.
What are the possible solutions for these image annotation issues?
We can effectively narrow down the solutions into 2 parts:
Employ different annotators
It’s good to start by having different expert annotators to work on and label the same data, hence reducing the chance of human errors.
Quality control We can create a system of examining annotation quality through routine reviews, spot checks, and provide regular feedback.
Wrapping up
This brings us to a close on some of the potentials, challenges and solutions for image annotation for augmented reality in the arena of eCommerce. The process of data scraping for the use of machine learning is a slippery slope. So, it is necessary to be transparent about these operations, protect user data as much as possible, and implement strict artificial intelligence laws.
