MLRichter/receptive_field_analysis_toolbox
A toolbox for receptive field analysis and visualizing neural network architectures
This tool helps machine learning engineers and researchers quickly analyze and optimize convolutional neural network architectures. It takes your existing PyTorch or TensorFlow/Keras model as input and outputs a visual graph that highlights layers predicted to be inefficient for a given input image resolution. This allows you to identify and fix potential issues before costly model training, especially when working with lower resolution images.
116 stars.
Use this if you need to rapidly check and visualize your neural network's architecture for inefficiencies, especially when adapting models for different image resolutions or optimizing performance without extensive training.
Not ideal if you need to analyze models with complex looping logic in their forward pass or if you rely heavily on the functional API for stateful operations in PyTorch.
Stars
116
Forks
5
Language
Python
License
MIT
Category
Last pushed
Nov 19, 2025
Commits (30d)
0
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