# Understanding the Philosophy Behind OpenCV's DNN Module **URL:** \ **Category:** Uncategorized\ **Tags:** dnn, tensorflow, onnx\ **Created:** [April 27, 2025, 3:10pm UTC](https://forum.opencv.org/t/understanding-the-philosophy-behind-opencvs-dnn-module/20906 "2025-04-27T15:10:35Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![radiantb](https://avatars.discourse-cdn.com/v4/letter/r/a87d85/32.png) [@radiantb](https://forum.opencv.org/u/radiantb)\ **Post date:** [April 27, 2025, 3:10pm UTC](https://forum.opencv.org/t/understanding-the-philosophy-behind-opencvs-dnn-module/20906/1 "2025-04-27T15:10:35Z")
Hello everyone, Sometime back, I was working on a proposal for a GSoC project about integrating LightGlue and ALIKED into OpenCV by using the DNN module and extending Feature2D modules. While experimenting with LightGlue ONNX models, I encountered challenges like lack of dynamic input shape support and unsupported operations (like ScatterND, GridSample, logical layers like `And`, `Or`, etc.). This made me wonder: - Given that frameworks like ONNX Runtime and TensorFlow already provide C++ APIs for running deep learning models, what was the core motivation behind creating the OpenCV DNN module? - Was it mainly for ease of integration into OpenCV pipelines (image processing, feature matching, etc.), portability across CPU architectures, minimal dependencies, or something else? - Was one of the original goals to enable efficient inference across different CPU architectures (x86, ARM, etc.) with the same lightweight C++ codebase, without heavy external dependencies? - How should one decide when it is appropriate to use OpenCV DNN versus integrating external runtime libraries? I would love to understand the original design and what kinds of problems OpenCV DNN is best suited to solve. Sorry if these are very basic questions — I’m just trying to learn the background properly. Thank you!