Product Image Tagging & Classification

This task paints products in pixels—think “red” tagged on a bag or “cotton” boxed on a tee (e.g., “small” marked, “metal” flagged)—to train AI to see goods like shoppers do. Our team annotates these visuals, sharpening search and recs with a keen eye.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are pivotal in orchestrating the annotation and structuring of data for Product Image Tagging & Classification within retail AI workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label datasets that enhance AI’s ability to recognize and categorize product images effectively.

Training and Onboarding

PMs design and implement training programs to ensure workers master attribute tagging, category annotation, and visual classification. For example, they might train teams to tag “leather” on a shoe or mark “electronics” on a gadget, guided by sample images and e-commerce standards. Onboarding includes hands-on tasks like annotating product shots, feedback loops, and calibration sessions to align outputs with AI recognition goals. PMs also establish workflows, such as multi-angle reviews for tricky visuals.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 15,000 product images) and set metrics like attribute accuracy, category precision, or visual consistency. They track progress via dashboards, address annotation errors, and refine methods based on worker insights or evolving retail needs.

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high detail for small features) into actionable annotation tasks. They also manage timelines, ensuring labeled datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The annotators, taggers, or image analysts perform the detailed work of labeling and structuring product image datasets for AI training. Their efforts are visual and descriptive, requiring precision and retail awareness.

Labeling and Tagging

For image data, we might tag traits as “color” or “shape.” In complex tasks, they label specifics like “striped pattern” or “rubber sole.”

Contextual Analysis

Our team decodes shots, tagging “large” on a box or marking “wood” on a frame, ensuring AI spots every product detail.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “green” as “blue”) or unclear data (e.g., blurry pics), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like faded colors or odd angles—often requiring zoom analysis or escalation to visual experts.

We can quickly adapt to and operate within our clients’ e-commerce platforms, such as proprietary imaging tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of images per shift, depending on the complexity of the visuals and annotations.

Data Volumes Needed to Improve AI

The volume of annotated image data required to enhance AI systems varies based on the diversity of products and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional image model might require 5,000–20,000 annotated images per category (e.g., 20,000 apparel shots). For varied or niche items, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 images per issue (e.g., missed tags) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., multi-category stores) require datasets in the hundreds of thousands to handle edge cases, rare features, or new stock. An annotation effort might start with 100,000 images, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky images for further annotation. This reduces total volume but requires ongoing effort—perhaps 500–2,000 images weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and visual precision across datasets.

Multilingual & Multicultural Product Image Tagging & Classification

We can assist you with product image tagging and classification across diverse linguistic and cultural landscapes.

Our team is equipped to label and analyze image data from global retail markets, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: