Lightly Blog

CLIP and Friends: How Vision-Language Models Evolved

Explore proven techniques for prompt engineering, addressing the technical foundations and practical implementations that drive successful AI interactions.

12 Best Data Annotation Tools for Computer Vision (Free & Paid)

Discover the 12 best data annotation tools for computer vision, including both free and paid options. This guide covers top labeling software for image and video annotation, helping AI teams improve model accuracy and efficiency.

The Engineer's Guide to Self-Supervised Learning

Learn what self-supervised learning is and how engineers can use it to train AI models with minimal labeled data. This guide explores key techniques, real-world applications, and the benefits of self-supervised learning in computer vision and machine learning.

Top Computer Vision Tools for ML Engineers in 2025

Discover the leading computer vision tools of 2025 in data labeling, curation, model development, deployment, and MLOps. An in-depth, technical review for ML engineers seeking the best open-source and enterprise solutions.

Efficient Training for Multimodal Vision Models: Techniques and Trade-offs

This article explores the evolution and key design choices in training multimodal vision language models (VLMs). It examines two main architectural approaches: cross-attention (pioneered by Flamingo) and self-attention (used in FROMAGe and BLIP2). We highlight how most modern VLMs build upon pre-trained unimodal backbones rather than training from scratch and discuss various techniques to boost performance, including masked training and resolution adaptation. It also outlines the typical three-stage training process: pre-training, supervised fine-tuning, and alignment, each serving distinct purposes in model development.