--- title: Understanding computer vision & AI, part 1 | daily.dev description: An introductory blog series on computer vision and AI, covering the fundamentals of object detection and building edge-based systems. It outlines a three-step... canonical: https://daily.dev/posts/understanding-computer-vision-ai-part-1-mnhwfag92 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Understanding computer vision & AI, part 1 | daily.dev og:description: An introductory blog series on computer vision and AI, covering the fundamentals of object detection and building edge-based systems. It outlines a three-step... og:url: https://daily.dev/posts/understanding-computer-vision-ai-part-1-mnhwfag92 og:image: https://api.daily.dev/og/posts/MNHWFAG92.png og:image:alt: Understanding computer vision & AI, part 1 og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Understanding computer vision & AI, part 1 **[Collabora Blog](https://daily.dev/sources/collabora)** · 4 min read · 0 upvotes · 0 comments ## Summary An introductory blog series on computer vision and AI, covering the fundamentals of object detection and building edge-based systems. It outlines a three-step process for developing a computer vision system: data collection and preparation, model training using gradient descent and hyperparameter tuning, and model evaluation. The post also discusses challenges of deploying complex networks like SSD, R-CNN, and Mask R-CNN on edge and mobile devices, and includes a demo using models trained on the COCO dataset for object detection and semantic segmentation. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What are the main challenges of deploying object detection models like SSD or Mask R-CNN on edge devices? SSD, R-CNN, Mask R-CNN, and similar networks achieve strong detection accuracy but are very difficult or impossible to deploy on edge and mobile devices due to computational and memory constraints. This limits their use in applications like robotics, video surveillance, and autonomous driving where local embedded processing is required. _Engineers targeting edge deployment track the latest lightweight model developments on daily.dev._ ### What dataset are common object detection models trained on and how large is it? Many object detection models are trained on the COCO dataset (Common Objects in Context), a large-scale benchmark consisting of 80,000 training images and 40,000 validation images across 80 object classes, also supporting segmentation and captioning tasks. _Developers building vision pipelines find dataset and model benchmarking discussions on daily.dev._ ## Similar posts on daily.dev - [The Beginner’s Guide to Computer Vision with Python](https://daily.dev/posts/the-beginner-s-guide-to-computer-vision-with-python-eporx2ra6) · Machine Learning Mastery · 2 upvotes · 0 comments - [CNNs, Explained Like You’ve Never Heard of Machine Learning](https://daily.dev/posts/cnns-explained-like-you-ve-never-heard-of-machine-learning-7zalqz6s0) · Medium · 0 upvotes · 0 comments - [AI On: 3 Ways to Bring Agentic AI to Computer Vision Applications](https://daily.dev/posts/ai-on-3-ways-to-bring-agentic-ai-to-computer-vision-applications-26pamoqrp) · NVIDIA · 0 upvotes · 0 comments - [OpenCV explained: The computer vision library for AI apps](https://daily.dev/posts/opencv-explained-the-computer-vision-library-for-ai-apps-9jetigq0a) · InfoWorld · 0 upvotes · 0 comments --- Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#computer-vision](https://daily.dev/tags/computer-vision), [#tensorflow](https://daily.dev/tags/tensorflow), [#object-detection](https://daily.dev/tags/object-detection) [View this post on daily.dev](https://daily.dev/posts/understanding-computer-vision-ai-part-1-mnhwfag92) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Understanding computer vision & AI, part 1","url":"https://daily.dev/posts/understanding-computer-vision-ai-part-1-mnhwfag92","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/understanding-computer-vision-ai-part-1-mnhwfag92"},"datePublished":"2026-08-11T08:56:14.778Z","dateModified":"2026-08-11T09:33:20.630Z","description":"An introductory blog series on computer vision and AI, covering the fundamentals of object detection and building edge-based systems. 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