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SmartLook

SmartLook

Agency
USA flagSheridan, Usa
smartlook-ai.com
$6.00/hrExpert200+

Key Skills

Software

CVATCVAT
LabelboxLabelbox
SuperviselySupervisely

Top Subject Matter

No subject matter listed

Top Data Types

ImageImage
VideoVideo

Top Task Types

No task types listed

Company Overview

Mission Empower enterprises with accurate, secure, and scalable image recognition and data labeling services, enabling faster AI adoption and flawless retail execution. Services Data Annotation & Labeling (bounding box, polygon, segmentation, video, classification, etc.) Additional Services Retail Image Recognition Engine – on-device, offline AI for shelf execution Continuous Delivery (MLOps) – automated retraining, dataset updates, QA monitoring

ExpertEnglishRussianSpanishTurkishChinese Mandarin

Security

Security Overview

Cybersecurity Policies Encrypted communication channels (SSL/TLS) for all data transfers Secure network infrastructure with firewalls, intrusion detection, and endpoint protection Antivirus and anti-malware software continuously updated On-premises or private cloud deployment options to meet client needs Employee Confidentiality & Data Handling All employees and annotators sign strict NDAs Mandatory data privacy and compliance training for all staff Role-based access controls ensuring employees only access the data required for their tasks Prohibition of external data storage or transfer outside secure systems Audits & Compliance Regular security audits and compliance reviews QA trails, gold set validation, and detailed audit logs for labeling projects Continuous monitoring to detect and address potential vulnerabilities

Labeling Experience

CVAT

SHELF INTELLIGENCE

CVATImageBounding BoxClassification
This annotation project involves labeling retail shelf images to support automated planogram compliance, product recognition, and retail analytics. Annotators are required to identify and mark key structural and product elements in supermarket or store shelf photos. The data produced is intended to train computer vision models for use in shelf monitoring, stock tracking, planogram enforcement, and visual merchandising optimization in retail environments

This annotation project involves labeling retail shelf images to support automated planogram compliance, product recognition, and retail analytics. Annotators are required to identify and mark key structural and product elements in supermarket or store shelf photos. The data produced is intended to train computer vision models for use in shelf monitoring, stock tracking, planogram enforcement, and visual merchandising optimization in retail environments

2023
CVAT

CHIPS DEFECTS

CVATImageBounding BoxClassification
This project involves annotating image data to detect and classify defects in food products - specifically potato chips - on a production line. Each chip in the image is outlined and categorized based on visual quality. Good-quality chips are marked with green contours, while defective ones are outlined in red, indicating issues such as burnt areas, discoloration, or structural defects. The annotated data serves as a foundation for training and evaluating computer vision models for automated quality control in food manufacturing

This project involves annotating image data to detect and classify defects in food products - specifically potato chips - on a production line. Each chip in the image is outlined and categorized based on visual quality. Good-quality chips are marked with green contours, while defective ones are outlined in red, indicating issues such as burnt areas, discoloration, or structural defects. The annotated data serves as a foundation for training and evaluating computer vision models for automated quality control in food manufacturing

2024 - 2024
CVAT

Product Packaging

CVATImageBounding BoxClassification
Project focuses on annotating product packaging data for retail and e-commerce applications. The goal is to label specific visual elements on packaging—such as brand logos, artwork, QR codes, UPCs, commercial names, claims, nutrition panels, and recycling symbols—to support tasks like automated product recognition, content extraction, compliance checks, and digital shelf organization. The labeled data will enable training and evaluation of computer vision models to accurately detect and classify packaging components across different layouts, languages, and product types.

Project focuses on annotating product packaging data for retail and e-commerce applications. The goal is to label specific visual elements on packaging—such as brand logos, artwork, QR codes, UPCs, commercial names, claims, nutrition panels, and recycling symbols—to support tasks like automated product recognition, content extraction, compliance checks, and digital shelf organization. The labeled data will enable training and evaluation of computer vision models to accurately detect and classify packaging components across different layouts, languages, and product types.

2023 - 2024
CVAT

PLANT DETECTION

CVATImageBounding BoxClassification
The project is aimed at annotating images for plant identification. Each specimen is marked with a rectangle and used in the training dataset. Only well-distinguishable plants with leaves and stems are annotated; fully dried-out or questionable objects (stones, branches, fallen leaves) are excluded. In cases of partial yellowing, the plant is annotated as a whole. If overlapped, only whole leaves are highlighted; leaf tips are not annotated. Plants at the edge of the frame are also considered. In dense thickets, clearly visible specimens are highlighted, or a common bounding box is used.

The project is aimed at annotating images for plant identification. Each specimen is marked with a rectangle and used in the training dataset. Only well-distinguishable plants with leaves and stems are annotated; fully dried-out or questionable objects (stones, branches, fallen leaves) are excluded. In cases of partial yellowing, the plant is annotated as a whole. If overlapped, only whole leaves are highlighted; leaf tips are not annotated. Plants at the edge of the frame are also considered. In dense thickets, clearly visible specimens are highlighted, or a common bounding box is used.

2023 - 2023