Displaying 1 to 20 from 65 results

nude.js - Nudity detection with JavaScript and HTMLCanvas

  •    Javascript

nude.js is a JavaScript implementation of a nudity scanner based on approaches from research papers. HTMLCanvas makes it possible to analyse image data and afterwards decide whether it should be displayed or not. The detection algorithm runs at the client, therefore it's possible (with user interaction) to display the image even if it's identified as nude (false positive) The real world usage for client side nudity detection could be in webproxies with child security filters, and maybe even more (e.g. on social media plattforms) nude.js is Open Source. Contributions are very welcome, the goal is to build a reliable client-side nudity scanner.Test the nudity detection script on several predefined images, I didn't have enough time to build a nice demo with flickr image support but feel free to test some of your images too. nude.js is currently supported in IE9(excanvas), FF 3.6+, Chrome, Safari and Opera. For really fast results try Chrome.

have-fun-with-machine-learning - An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks

  •    Python

Also available in Chinese (Traditional). This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic.




darts - Differentiable architecture search for convolutional and recurrent networks

  •    Python

DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arXiv:1806.09055. NOTE: PyTorch 0.4 is not supported at this moment and would lead to OOM.


tf_trt_models - TensorFlow models accelerated with NVIDIA TensorRT

  •    Python

This repository contains scripts and documentation to use TensorFlow image classification and object detection models on NVIDIA Jetson. The models are sourced from the TensorFlow models repository and optimized using TensorRT. Flash your Jetson TX2 with JetPack 3.2 (including TensorRT).

mmclassification - OpenMMLab Image Classification Toolbox and Benchmark

  •    Jupyter

MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project. This project is released under the Apache 2.0 license.

CV-pretrained-model - A collection of computer vision pre-trained models.

  •    

A pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self learning car. You can spend years to build a decent image recognition algorithm from scratch or you can take inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures.

PaddleClas - A treasure chest for visual recognition powered by PaddlePaddle

  •    Python

PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios. A practical image recognition system consist of detection, feature learning and retrieval modules, widely applicable to all types of image recognition tasks. Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition and animation character recognition.

autogluon - AutoGluon: AutoML for Text, Image, and Tabular Data

  •    Python

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on text, image, and tabular data. Announcement for previous users: The AutoGluon codebase has been modularized into namespace packages, which means you now only need those dependencies relevant to your prediction task of interest! For example, you can now work with tabular data without having to install dependencies required for AutoGluon's computer vision tasks (and vice versa). Unfortunately this improvement required a minor API change (eg. instead of from autogluon import TabularPrediction, you should now do: from autogluon.tabular import TabularPredictor), for all versions newer than v0.0.15. Documentation/tutorials under the old API may still be viewed for version 0.0.15 which is the last released version under the old API.

Labelbox - The most versatile data labeling platform for training expert AI.

  •    TypeScript

Labelbox is a data labeling tool that's purpose built for machine learning applications. Start labeling data in minutes using pre-made labeling interfaces, or create your own pluggable interface to suit the needs of your data labeling task. Labelbox is lightweight for single users or small teams and scales up to support large teams and massive data sets. Simple image labeling: Labelbox makes it quick and easy to do basic image classification or segmentation tasks. To get started, simply upload your data or a CSV file containing URLs pointing to your data hosted on a server, select a labeling interface, (optional) invite collaborators and start labeling.

cvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

  •    Javascript

CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.

tensornets - High level network definitions with pre-trained weights in TensorFlow

  •    Python

High level network definitions with pre-trained weights in TensorFlow (tested with >= 1.1.0). You can install TensorNets from PyPI (pip install tensornets) or directly from GitHub (pip install git+https://github.com/taehoonlee/tensornets.git).

channel-pruning - Channel Pruning for Accelerating Very Deep Neural Networks

  •    Python

Please have a look at AMC: AutoML for Model Compression and Acceleration on Mobile Devices ECCV'18, which combines channel pruning and reinforcement learning to further accelerate CNN.

bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc

  •    Python

Embed everything, thanks to AI, we can use neural networks to extract feature vectors from unstructured data, such as image, audio and vide etc. Then analyse the unstructured data by calculating the feature vectors, for example calculating the Euclidean or Cosine distance of the vectors to get the similarity. Milvus Bootcamp is designed to expose users to both the simplicity and depth of the Milvus vector database. Discover how to run benchmark tests as well as build similarity search applications like chatbots, recommender systems, reverse image search, molecular search, video search, audio search, and more.






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