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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 main features of yahoo/open_nsfw are: NSFW Content Filters, Content Moderation Filters, Caffe Framework Implementations, Convolutional Neural Networks, Deep Learning Classifiers, Image Classification, NSFW Classifications, Vision Detection Model Training.
Projects with overlapping indexed features include: humphd/have-fun-with-machine-learning — This project is a neural network image classifier and a set of tools for building and training convolutional neural… infinitered/nsfwjs — NSFW detection on the client-side via TensorFlow.js. hunkim/deeplearningzerotoall — DeepLearningZeroToAll is a comprehensive educational resource and implementation collection focused on deep learning… binroot/tensorflow-book — This project is a collection of TensorFlow machine learning examples providing reference implementations for various… dennybritz/cnn-text-classification-tf — This project is a TensorFlow implementation of a convolutional neural network designed for text classification. It… morvanzhou/pytorch-tutorial — This project is a collection of PyTorch learning resources and educational guides designed to teach the construction…
This project is a neural network image classifier and a set of tools for building and training convolutional neural networks to recognize and categorize images. It serves as a machine learning educational guide, providing a practical resource for learning neural network fundamentals through an onboarding process. The system includes a dedicated workflow for pretrained model fine-tuning, allowing existing network weights to be adapted to new image categories. This is supported by a transfer learning pipeline that replaces final classification layers and adjusts weights through targeted retrain
NSFW detection on the client-side via TensorFlow.js
DeepLearningZeroToAll is a comprehensive educational resource and implementation collection focused on deep learning and machine learning. It provides a structured learning path using TensorFlow to move from foundational linear models to complex neural network architectures. The project is distinguished by its practical implementations of various network types, including multilayer perceptrons for logic problems, convolutional neural networks for spatial data and image recognition, and recurrent neural networks using LSTM cells for time-series forecasting and character sequence prediction. It
This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders