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This project is a collection of pre-trained machine learning models and conversion pipelines designed for running inference directly in the browser using TensorFlow.js. It provides a library of ready-to-use models for computer vision, audio classification, and natural language processing tasks. The suite includes specialized tools for transforming Python-based Keras models into JSON formats compatible with web environments. It enables the deployment of these models by fetching architectures and weight shards via HTTP for client-side execution. The project covers a broad range of capabilities
TensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute models. It functions as a WebGL accelerated tensor engine, providing a foundation for high-performance linear algebra operations and an automatic differentiation framework for computing gradients. The project distinguishes itself through its ability to run machine learning directly in web environments, supporting both client-side inference and browser-based training. It enables the deployment of Python-based models by converting Keras or TensorFlow models into compatible formats
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
Open NSFW is a deep learning image classifier and content filter designed to identify explicit or unsafe visual content. It uses a neural network to analyze images and generate probability scores that determine whether media is not safe for work. The system employs a Caffe-based model architecture to perform automated visual feature extraction and image classification. It supports the use of pre-trained weights and allows for fine-tuning the network with custom datasets to adjust detection accuracy. The project covers end-to-end image moderation workflows, including automated content scannin
NSFW detection on the client-side via TensorFlow.js
The main features of infinitered/nsfwjs are: NSFW Classifications, NSFW Classifiers, Client-Side Inference, TensorFlow.js Models, Image Classification, Image Data Preprocessing, Bundled Weight Embeddings, Client-Side Model Loading.
Open-source alternatives to infinitered/nsfwjs include: tensorflow/tfjs-models — This project is a collection of pre-trained machine learning models and conversion pipelines designed for running… tensorflow/tfjs — TensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and… tensorflow/tfjs-core — TensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute… yahoo/open_nsfw — Open NSFW is a deep learning image classifier and content filter designed to identify explicit or unsafe visual… xenova/transformers.js — Transformers.js is a JavaScript library and web machine learning framework designed to run pretrained transformer… harthur/brain — Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a…