Sign in

Michael Abramov

Michael Abramov

Computer Vision

Handling Cultural and Language Nuances in Annotation Teams

When building datasets, annotation teams often come from different cultural and linguistic backgrounds. This diversity can provide valuable perspectives but also leads to differences in how people understand the same task. Language is also closely related to the local context. Annotators may understand words but not their meanings if they

Computer Vision

Annotating for OCR: Preparing Datasets for Text Extraction in Images

In the emerging field of computer vision, the ability to extract text from images has opened the door to automation, accessibility, and data-driven insights in countless industries. From digitizing handwritten archives to real-time translation using mobile devices, systems built on optical character recognition (OCR) have become more sophisticated and widespread.

Computer Vision

GDPR and Data Labeling: Best Compliance Practices for EU Markets

Organizations that process personal data understand the impact of GDPR on data protection and labeling practices. The GDPR, established in 2018, ensures data integrity and confidentiality. However, labeling personal data in a data protection-compliant manner can be challenging, especially when working with raw data. Quick Take * Accurate labeling of personal

Computer Vision

Implementing Active Learning Strategies: Label Smarter, Not Harder

Instead of randomly labeling a large dataset, active learning helps identify the most informative data points, reducing the overall labeling effort and improving model performance. Compared to traditional supervised learning, where all data points are labeled before training, active learning allows the model to request labels for only the most

Data Annotation

Implementing CI/CD for Data Annotation: Practices and Tools

At the heart of CI/CD is automation. Automated pipelines accelerate the delivery of new features and ensure that every change is thoroughly tested, reducing the risk of errors in production. A correctly implemented CI/CD pipeline for data annotation streamlines workflows and improves collaboration by unifying coding, testing, and

Machine Learning

Agriculture AI: Improving Crop Management and Yield Predictions

Agriculture AI is changing the field of agriculture. Drones, sensors, and satellite imagery are used to annotate data. This makes it easier to manage farms and crops and predict yields. This innovation fights climate change and food security, leading to sustainable agriculture. Quick Take * Precision farming annotation increases yields through

Computer Vision

Augmenting Datasets for Rare Object Classes: A Practical Guide

The quality and diversity of training data affect the performance and reliability of AI models and computer vision tasks. We will review methodologies and state-of-the-art tools to enrich datasets with rare or smaller classes. These methodologies integrate information from various sources to identify strategic approaches to data collection, the importance