Brand Recognition Through Image Labeling
The team delivered excellent work! Their strong communication and sense of responsibility ensured that the project stayed on track and produced a reliable dataset, significantly enhancing our machine learning models.
Spokesperson, IT Company
Key Results
10000+ images labeled
95% accuracy rate achieved
Project Overview
The project was centered on performing image labeling tasks using the Labelbox platform, specifically aimed at identifying and assigning the correct brand labels within a diverse set of website screenshots. These screenshots featured various brand logos, and our objective was to accurately match each logo with its corresponding brand name from a provided list. This meticulous labeling process was designed to simulate the training of a machine learning model in recognizing and classifying brand logos. By carefully annotating the images, we contributed valuable data that supports the development and refinement of advanced image recognition technologies. This project not only enhanced the model’s ability to differentiate between brands but also played a critical role in advancing the overall effectiveness of machine learning systems in visual recognition tasks.
Business Problem
The project tackled the complex challenge of ensuring precise labeling of brand logos within website screenshots to create a high-quality dataset essential for training machine learning algorithms. This process required meticulous verification to ensure that each screenshot was accurately classified into its corresponding brand dataset. Additionally, the project involved identifying and documenting false positives, which are incorrectly labeled items, and addressing potential confusion between brands that have similar visual elements. By managing these aspects effectively, the project aimed to enhance the dataset’s reliability and usability, thereby improving the performance and accuracy of machine learning models in distinguishing and recognizing different brand logos. This rigorous approach was crucial for developing advanced image recognition systems capable of accurately identifying and categorizing brand logos in diverse contexts.
Solutions Delivered to the Client
Our team undertook an extensive and detailed labeling process for each brand dataset, carefully examining and classifying screenshots to maintain the highest standards of accuracy and consistency. Each screenshot was meticulously reviewed to ensure that the correct brand labels were applied, with rigorous checks in place to identify and document false positives—images that were incorrectly labeled—for further analysis and correction. To mitigate confusion between brands with similar visual elements, we implemented specific measures designed to enhance the clarity and precision of the labeling process. These efforts were crucial in producing a reliable and comprehensive dataset that is essential for effectively training machine learning models. The dataset we created plays a pivotal role in improving the models' ability to recognize and differentiate between brand logos with high accuracy, ultimately contributing to the advancement of image recognition technologies.
People involved
Mariia
Project Manager
Mariia
Internet Research Specialist
Iryna
Internet Research Expert
