You’re working on a project with lots of visual data. How can you identify the most important parts?

What is image recognition?

Did you know that the same technology used to recognize your friend's face in a photo is also used to identify cancer cells?

It's called image recognition, and it's everywhere. 

You can find it in the facial recognition feature that unlocks our smartphones with just a glance. It's also used in mobile banking apps to deposit checks easily & securely and process the necessary information for quick transactions.

Image recognition technology is not limited to personal use. It's also important in the logistics and moving industry. At Yembo, we offer AI visual surveys, which automatically create a visual inventory for moving. This visual inventory shows color-coded photos of each room, summarizing the inventory of the move for an easy & efficient process.

Why use image recognition tools?

Drawing inspiration from the age-old wisdom that "Necessity is the mother of invention," it's fascinating to witness how technology evolves to address our most pressing challenges. 

Image recognition, a testament to this principle, has emerged as a groundbreaking solution to tackle the mundane, repetitive, and time-consuming aspects of human tasks. As more and more organizations use this tool, it helps them process visual data and reduces the workload of people in different industries. 

The use of machine learning and the increased capacity of computers have put image recognition in the spotlight. This technology can automate tedious tasks and improve efficiency, providing a better future where machines help humans with complex visual tasks.

How to choose the right image recognition tool?

When it comes to image recognition tools, it's important to know the signs of a bad one. Here are five red flags to look out for:

Low-quality image recognition tools may misidentify objects or provide a high number of false positives.

An inadequate tool may lack the flexibility to adapt to different use cases or industries. 

A reliable image recognition tool should excel with diverse visual data types, such as images taken under different lighting conditions, angles, or resolutions. 

A substandard tool may pose challenges when integrating with existing systems or platforms. 

A tool that falls behind in updates and support may not provide the necessary features or security measures, compromising its overall performance.

How to use the image recognition tool?

Each tool has its features and functions, so it's essential to tailor your approach based on your project's scope and the type of tool you've chosen. 

Start by preparing and uploading your visual data to ensure it meets the tool's requirements. Then, select and configure the parameters and options that align with your project goals. Whether you're fine-tuning for accuracy or optimizing for speed, you need to understand the tool's capabilities. 

Once configured, run the tool and carefully analyze the results. Depending on the tool, you may need to export or download the outcomes for further analysis or integration into your systems. Always refer to the tool's documentation for specific steps and processes.

How to improve the image recognition tool?

As the wise saying goes, "In the journey of improvement, even the smallest steps count." Improving an image recognition model requires three key steps: expanding the dataset, using data augmentation, and embracing cross-validation. 

The dataset needs to be large enough to provide ample training data for neural network models to improve accuracy. 

Data augmentation, which involves introducing subtle variations like mirror images, angle adjustments, or grayscale modifications, can also help boost accuracy, especially in scenarios where datasets are limited. 

Lastly, cross-validation through methods like k-fold can refine the model and provide a robust assessment by averaging scores across iterations.