Computer Vision Hub

AI Edge Deployment

Discover deployment patterns, optimization for production, and scaling computer vision applications.

Edge Deployment

Model Drift

Active Learning Loops

Data Collection & Curation

Model Training

Explore training techniques, optimization strategies, and best practices for CV model development.

Data

Learn about dataset preparation, annotation, and quality assessment for computer vision projects.

Data Annotation

Data Visualization

How High is your ML Maturity & How to Increase It?

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Lightly Tutorials & Benchmarks

Hands-on resources and deep-dives to accelerate your computer vision projects

View LightlyTrain Tutorials

Curate a Traffic CCTV Dataset for YOLO Training

Build and train an embedding model for satellite imagery using ResNet and Lightly workflows.

Evaluate YOLO26 on your dataset with LightlyStudio

Learn how to train a depth estimation model using fastai’s U-Net and Lightly.

YOLO vs. Transformer-based Object Detection Model

A technical comparison of YOLO and transformer-based object detection models, covering architecture tradeoffs, the structural limits of CNN-based detectors, and a practical decision framework for choosing between YOLO and DINO-backed transformer architectures.

Lightly RL Environments Catalogue 2026

A practical catalogue of Lightly's reinforcement learning environments for AI agents, covering BI tools, finance, IT operations, and data science workflows with benchmark results across Claude and Gemini models.

The ML Engineer's Guide to DINO, DINOv2, DINOv3

A technical guide to DINO, DINOv2, and DINOv3, covering the training objectives, architectural innovations, and scaling tradeoffs across Meta AI's self-supervised vision foundation model family.

Computer Vision Architecture Decision Tree

A practical decision tree cheatsheet for choosing the right computer vision architecture. Compare detection, segmentation, classification, and keypoint models based on accuracy, latency, and deployment needs.

Beyond ImageNet: Smarter Model Pretraining for Real-World Vision Tasks

See benchmark results proving smarter pretraining beats ImageNet. Discover how domain‑specific models excel in real‑world AI vision tasks.

YOLO vs. Transformer-based Object Detection Model

Lightly RL Environments Catalogue 2026

The ML Engineer's Guide to DINO, DINOv2, DINOv3

A technical guide to DINO, DINOv2, and DINOv3, covering the training objectives, architectural innovations, and scaling tradeoffs across Meta AI's self-supervised vision foundation model family.