Explainable YOLOv8: Explaining YOLOv8 Results Using Eigen-CAM
Last Updated on November 22, 2023 by Editorial Team
Author(s): Sumit Pandey
Originally published on Towards AI.
I am a big fan of YOLO (You Only Look Once) models, especially version 8. It is easy to train and deploy, even in the medical imaging domain. I recently finished a classification problem using YOLOv8, and it worked quite well. However, the main issue was its lack of an inbuilt Explainable results function like GRAD-CAM or Eigen-CAM. After searching on the internet for hours, I found a GitHub repository that does exactly what I wanted, and even more. It is also easy to use. So, let’s see how to use:
IntroductionStep cloning github repositoryImporting librariesInitializing EigenCAM
The integration of Eigen-CAM into… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
