Audio Transcription
Datasets and Services for Training LLM
Image Annotation USE CASE
Makeup Before/After Image Collection
A dataset of photos of people with and without makeup for cosmetics recognition
Computer Vision Ability of a machine to interpret, analyze, and understand visual data
AR Machine learning in augmented reality applications
Re-identification Recognition of the same object in different observations
Classification Process of recognition and grouping of objects into preset categories
2 000
with makeup and real photos without makeup
3 weeks
project duration
Our Partners
CASE DESCRIPTION
Collection of photos of people with makeup and real photos without makeup through crowdsourcing. One type of cosmetics per person: lipstick, eyeliner, eyeshadow
Facial classification based on makeup type, tagged in CVAT (Computer Vision Annotation Tool). Semantic segmentation of the face for detailed cosmetics recognition. Image matting for AR solutions
APPLICATION AREAS OF THE DATASET
Makeup Individualization:
Facial classification and cosmetics segmentation for personalized cosmetic recommendations based on a person's appearance
AR in Advertising:
Semantic segmentation and image matting for selfies to create advertising masks that demonstrate the effects of cosmetics in real time
Makeup Detection:
Classification of selfies with makeup to determine the presence of makeup in photos
Cosmetic Industry:
Crowdsourced research for evaluating new cosmetic products
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Why
Training Data
- Quality Assurance:
- Enhanced Data Accuracy
- Consistency in Labels
- Reliable Ground Truth
- Mitigation of Annotation Biases
- Cost and Time Efficiency
- Data Security and Confidentiality:
- GDPR Compliance
- Non-disclosure agreement
- Data Encryption
- Multiple data storage options
- Access Controls and Authentication
- Expert Team:
- 6 years in industry
- 35 top project managers
- 40+ languages
- 100+ countries
- 250k+ assessors
- Flexible and Scalable Solutions:
- 24/7 availability of customer service
- 100% post payment
- $550 minimum check
- Variable Workload
- Customized Solutions
Team leads project
Sergey Razumny
TeamLead Crowd Solutions
Arthur Kazukevich
Python-developer
Wadim Starosotnikow
Senior quality control manager