Overview
A retail technology company building a computer vision platform partnered with Sourcefit to improve the accuracy and consistency of its AI training datasets. The platform analyzes in store shelf images to detect product placement, out of stock conditions, and merchandising issues. To strengthen model performance, the client needed large volumes of accurately tagged images and a reliable human in the loop quality assurance process.
Sourcefit designed and now manages a dedicated annotation program that handles image tagging, visual QA checks, and quality verification across thousands of shelf images. The team operates under a transparent cost plus model with consistent accuracy, stable throughput, and structured production workflows.
The challenge
- Computer vision models required high quality annotations for products, shelves, pricing, and image level conditions
- Large training datasets needed a scalable team capable of consistent accuracy
- Tagging SOPs were detailed and required training to ensure correct segmentation and labeling
- Image level issues such as blur, glare, and angle changes needed to be identified
- The client needed reliable QA and calibration to maintain accuracy across complex retail environments
Our approach
Sourcefit built and operates a dedicated annotation and QA team trained directly on the client’s labeling platform and visual standards. The team was onboarded through a structured training program focused on retail image requirements, tagging precision, and gold set alignment.
During setup, Sourcefit:
- Conducted calibration sessions to align on product level tagging and shelf segmentation
- Established retail specific SOPs for detailed bounding box and polygon annotations
- Implemented multi tier QA with real time validation against gold sets
- Set up daily reporting on accuracy, throughput, and error trends
- Created feedback loops to improve tagger consistency across complex shelf images
Today, Sourcefit manages end to end image tagging, image level quality checks, and QA verification while the client uses the outputs for computer vision model training and continuous improvement.
Results
- Consistently surpassed the client’s 95% accuracy benchmark
- Strengthened training dataset quality for retail computer vision workflows
- Improved tagging consistency across varied store layouts and product sets
- Delivered stable, high volume image annotation with daily QA validation
- Enabled faster and more reliable model training cycles
Key takeaways
- Annotation quality drives performance: High accuracy image tagging is essential for training reliable shelf monitoring computer vision models.
- Clear standards support consistency: Detailed SOPs, calibration sessions, and gold set validation keep annotations aligned across large and complex retail datasets.
- Scalable teams enable growth: A dedicated annotation and QA team allows the client to increase training data volume without losing control over accuracy or throughput.
Industry learnings
Retail computer vision systems depend on strong annotation and QA operations to maintain accuracy in real world environments. Camera imagery varies widely across stores, fixtures, and lighting conditions, making detailed human review essential for model reliability. This engagement shows how structured image tagging and human in the loop validation help retail AI platforms improve detection, reduce noise, and accelerate training cycles.
Learn more
Sourcefit supports AI and computer vision companies with scalable annotation, QA, and data operations teams.
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