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poloclub/cnn-explainer

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View on GitHub↗
poloclub.github.io/cnn-explainer↗

Cnn Explainer

cnn-explainer is an interactive web application and educational sandbox designed for visualizing the internal operations and layers of convolutional neural networks. It functions as a tool for understanding how these networks process image data through real-time graphics and interactive visualizations.

The project includes a browser-based environment for training small convolutional neural networks on specific image classes. It also provides a model converter that transforms trained neural network files from backend framework formats into web-compatible versions for browser loading.

The application utilizes scalable vector graphics to render network architecture and feature maps. It implements client-side model inference, training loops, and tensor-based data flow to simulate convolutional operations directly in the browser.

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Features

  • Neural Network Visualizations - Provides an interactive web application for exploring the internal layers and weights of trained convolutional neural networks.
  • Client-Side Training - Implements backpropagation and gradient descent entirely within the browser to train small models.
  • Browser-Based Inference - Implements real-time neural network inference directly in the browser using a JavaScript tensor library.
  • Vision Model Training - Implements a training process for convolutional neural networks focused on image classification.
  • Neural Network Visualization Tools - Provides an interactive web application for visualizing the internal operations and layers of convolutional neural networks.
  • Interactive Sandboxes - Offers an interactive sandbox for experimenting with how CNNs process image data through real-time graphics.
  • Browser-Based Trainers - Ships a browser-based environment to train small convolutional neural networks on specific image classes.
  • Convolutional Neural Network Tutorials - Provides an interactive environment for learning how convolutional neural networks process images and perform operations.
  • Interactive Visualizations - Displays internal CNN operations through interactive graphics to help users understand the learning process.
  • Neural Architecture Visualizations - Renders network architecture and feature maps using scalable vector graphics for interactive inspection.
  • Educational Model Training - Enables training of small convolutional neural networks on specific image classes for educational visualization.
  • Model Format Converters - Transforms trained neural network files from backend framework formats into web-compatible versions.
  • Model Conversion - Transforms trained backend model files into web-compatible formats for browser-based visualization.
  • Cross-Framework Translation - Translates model weights from backend deep learning frameworks into a standardized JSON format for browser execution.
  • Computer Vision Tools - An interactive visualization tool for understanding convolutional neural networks.
  • Machine Learning Frameworks - Interactive visualization for learning convolutional neural networks.
  • Perception and Machine Learning - Interactive visualization for convolutional neural networks.
8,958 stars·1,343 forks·JavaScript·MIT·42 views

Star history

Star history chart for poloclub/cnn-explainerStar history chart for poloclub/cnn-explainer

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with Cnn Explainer

These projects share indexed features with Cnn Explainer. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • alibaba/mnnalibaba avatar

    alibaba/MNN

    14,242View on GitHub↗

    MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse

    C++armconvolutiondeep-learning
    View on GitHub↗14,242
  • tensorflow/playgroundtensorflow avatar

    tensorflow/playground

    12,939View on GitHub↗

    This project is a browser-based machine learning education tool and neural network sandbox. It provides an interactive environment for experimenting with network architectures and hyperparameters to understand deep learning concepts. The tool functions as a visualizer for TensorFlow neural networks, allowing users to see how models learn and classify data in real time. It enables the prototyping of model architectures to observe how different hidden layers and neurons affect a network's ability to solve specific data patterns. The system covers neural network architecture and operation visua

    TypeScript
    View on GitHub↗12,939
  • apple/corenetapple avatar

    apple/corenet

    6,999View on GitHub↗

    Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in gene

    Jupyter Notebook
    View on GitHub↗6,999
  • tensorflow/tfjstensorflow avatar

    tensorflow/tfjs

    19,134View on GitHub↗

    TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and server-side environments. It functions as a browser-based model trainer, a WebAssembly inference engine, and a WebGPU accelerated tensor library for low-level linear algebra. The project also includes a model converter to transform Python-based models into optimized formats for JavaScript execution. The library distinguishes itself through a pluggable backend architecture that allows mathematical operations to be executed via CPU, WebGL, or WebGPU. It supports the conversion of Py

    TypeScript
    View on GitHub↗19,134
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Frequently asked questions

What does poloclub/cnn-explainer do?

cnn-explainer is an interactive web application and educational sandbox designed for visualizing the internal operations and layers of convolutional neural networks. It functions as a tool for understanding how these networks process image data through real-time graphics and interactive visualizations.

What are the main features of poloclub/cnn-explainer?

The main features of poloclub/cnn-explainer are: Neural Network Visualizations, Client-Side Training, Browser-Based Inference, Vision Model Training, Neural Network Visualization Tools, Interactive Sandboxes, Browser-Based Trainers, Convolutional Neural Network Tutorials.

Which projects share features with poloclub/cnn-explainer?

Projects with overlapping indexed features include: alibaba/mnn — MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a… tensorflow/playground — This project is a browser-based machine learning education tool and neural network sandbox. It provides an interactive… apple/corenet — Corenet is a deep learning training framework and computer vision model library designed for developing neural… tensorflow/tfjs — TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and… brainjs/brain.js — Brain.js is a JavaScript neural network library for building, training, and running machine learning models in the… paddlepaddle/paddle — Paddle is a deep learning framework designed for building, training, and deploying neural networks. It provides a…