10,000+ images labeled at 95% accuracy

Overview

The project focused on labeling website screenshots to identify and assign the correct brand labels from a predefined list.

The labeled images were used to create a reliable dataset for training machine learning models to recognize and distinguish brand logos across different visual contexts.

The challenge

Precise brand identification — each screenshot needed to be accurately classified according to its corresponding brand dataset.
False positives — incorrectly labeled images had to be identified and documented to prevent unreliable data from entering the dataset.
Similar visual elements — brands with visually similar logos or elements created a risk of confusion and required additional verification.

What we did

1 Labeled website screenshots — carefully reviewed screenshots and assigned the correct brand labels according to the provided dataset.
2 Verified brand classifications — checked each screenshot to ensure that the assigned label matched the corresponding brand.
3 Identified false positives — documented incorrectly labeled images for further analysis and correction.
4 Addressed visual similarities — applied additional checks to reduce confusion between brands with similar visual elements and maintain dataset consistency.

Results

10,000+ images labeled — screenshots were systematically reviewed and assigned to the appropriate brand datasets.
95% accuracy rate achieved — careful verification helped maintain a high level of labeling accuracy.
False positives identified — incorrectly labeled images were documented for analysis and correction.
Reliable training dataset created — the labeled data supported the development and refinement of machine learning models for brand recognition.

Our methodology

How we structure image labeling for brand recognition

Reliable image labeling requires consistent classification, verification, and careful handling of ambiguous or incorrectly labeled examples.

Brand classification

Each screenshot is reviewed and assigned the appropriate brand label from the predefined dataset.

Label verification

Images are carefully checked to confirm that the assigned brand corresponds to the visual content.

False-positive detection

Incorrectly labeled images are identified and documented so they can be reviewed and corrected.

Visual similarity checks

Additional attention is given to brands with similar visual elements to improve classification accuracy and dataset consistency.

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