Displaying 1 to 20 from 42 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.


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.

hub - A library for transfer learning by reusing parts of TensorFlow models.

  •    Python

TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. In particular, it provides modules, which are pre-trained pieces of TensorFlow models that can be reused on new tasks. If you'd like to contribute to TensorFlow Hub, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

food-101-keras - Food Classification with Deep Learning in Keras / Tensorflow

  •    Jupyter

If you are reading this on GitHub, the demo looks like this. Please follow the link below to view the live demo on my blog. Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. determining whether a picture is that of a dog or cat.

SeeFood - Inspired by HBO's Silicon Valley: SeeFood is an iOS app that uses CoreML to detect various dishes

  •    Swift

For a step by step guide on how to build SeeFood: How to train your own model for CoreML. Xcode 9 (currently Version 9.0 beta 3 (9M174d)). The trained CoreML data model which can be downloaded here. An iOS device running iOS 11+.

SiaNet - An easy to use C# deep learning library with CUDA/OpenCL support

  •    CSharp

Developing a C# wrapper to help developer easily create and train deep neural network models. The below is a classification example with Titanic dataset. Able to reach 75% accuracy within 10 epoch.

lightnet - 🌓 Bringing pjreddie's DarkNet out of the shadows #yolo

  •    C

LightNet provides a simple and efficient Python interface to DarkNet, a neural network library written by Joseph Redmon that's well known for its state-of-the-art object detection models, YOLO and YOLOv2. LightNet's main purpose for now is to power Prodigy's upcoming object detection and image segmentation features. However, it may be useful to anyone interested in the DarkNet library. Once you've downloaded LightNet, you can install a model using the lightnet download command. This will save the models in the lightnet/data directory. If you've installed LightNet system-wide, make sure to run the command as administrator.

fashion - The Fashion-MNIST dataset and machine learning models.

  •    R

Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.