ASCII Video Projector « earthlingsoft Homepage Present films using characters Download (Version 3, 508KB, Mac OS X.4) Donationware buy us a drink – you know we want it. ASCII Projektor presents films by drawing them with characters. The application accepts films from the computer’s camera as well as from QuickTime files. Previous versions of ASCII Projektor played films in a Terminal window. If you are running Mac OS X.3, please download version 2 of the application which can only play films in the Terminal. Screenshots • E-Mail • Leave a comment • flickr group • Donate GAN Lab: Play with Generative Adversarial Networks in Your Browser! What is a GAN? Many machine learning systems look at some kind of complicated input (say, an image) and produce a simple output (a label like, "cat"). By contrast, the goal of a generative model is something like the opposite: take a small piece of input—perhaps a few random numbers—and produce a complex output, like an image of a realistic-looking face. A generative adversarial network (GAN) is an especially effective type of generative model, introduced only a few years ago, which has been a subject of intense interest in the machine learning community. You might wonder why we want a system that produces realistic images, or plausible simulations of any other kind of data. Besides the intrinsic intellectual challenge, this turns out to be a surprisingly handy tool, with applications ranging from art to enhancing blurry images. How does a GAN work? The first idea, not new to GANs, is to use randomness as an ingredient. What's happening in the visualization? Pick a data distribution. ).
Un programme pour transformer tous les dessins des internautes en photos de chat réalistes Un site propose à ses visiteurs d’esquisser les contours du félin, puis de lui donner une allure réaliste. Un procédé qui donne parfois des résultats surprenants, voire effrayants. C’est un petit programme ingénieux qu’a fabriqué le développeur Christopher Hesse. Image-to-Image permet aux internautes de rendre leurs dessins réalistes. Bien sûr, le résultat est loin d’être parfait... mais c’est justement ce qui fait son charme, et tout son intérêt. Lire aussi : On a testé pour vous… Deep Dream, la machine à « rêves » psychédéliques de Google Deux mille photos de chat pour « entraîner » le programme Basé sur Tensorflow, une technologie d’apprentissage des machines développée par Google et accessible à tous, le programme de Christopher Hesse s’est « entraîné » sur des photos déjà existantes. Cette logique est assez proche de celle qui avait déjà amusé les internautes en 2015, quand Google avait présenté son programme Deep Dream.
piq Continuous video classification with TensorFlow, Inception and Recurrent Nets Part 2 of a series exploring continuous classification methods. A video is a sequence of images. In our previous post, we explored a method for continuous online video classification that treated each frame as discrete, as if its context relative to previous frames was unimportant. Today, we’re going to stop treating our video as individual photos and start treating it like the video that it is by looking at our images in a sequence. We’ll process these sequences by harnessing the magic of recurrent neural networks (RNNs). To restate the problem we outlined in our previous post: We’re attempting to continually classify video as it’s streamed, in an online system. Convolutional neural networks, which we used exclusively in our previous post, do an amazing job at taking in a fixed-size vector, like an image of an animal, and generating a fixed-size label, like the class of animal in the image. Sold! Step 2 is unique so we’ll expand on it a bit. Softmax and pool layers? Frames to sequences
Incorporated | LICEcap LICEcapsimple animated screen captures LICEcap can capture an area of your desktop and save it directly to .GIF (for viewing in web browsers, etc) or .LCF (see below). LICEcap is an intuitive but flexible application (for Windows and now OSX), that is designed to be lightweight and function with high performance. LICEcap is easy to use: view a demo (output is here). In addition to .GIF, LICEcap supports its own native lossless .LCF file format, which allows for higher compression ratios than .GIF, higher quality (more than 256 colors per frame), and more accurate timestamping. LICEcap is GPL free software, each download package includes the source. Features and options: Record directly to .GIF or .LCF. Download LICEcap v1.32 for Windows (Jun 8 2022) (250kb installer)LICEcap v1.32 for macOS (Jun 8 2022) (876kb DMG) Windows: Prevent positioning window offscreen [issue 72] Windows: sign installer/executable Source codegit clone Old versions
Keras, Regression, and CNNs In this tutorial, you will learn how to train a Convolutional Neural Network (CNN) for regression prediction with Keras. You’ll then train a CNN to predict house prices from a set of images. Today is part two in our three-part series on regression prediction with Keras: Part 1: Basic regression with Keras — predicting house prices from categorical and numerical data.Part 2: Regression with Keras and CNNs — training a CNN to predict house prices from image data (today’s tutorial).Part 3: Combining categorical, numerical, and image data into a single network (next week’s tutorial). Today’s tutorial builds on last week’s basic Keras regression example, so if you haven’t read it yet make sure you go through it in order to follow along here today. By the end of this guide, you’ll not only have a strong understanding of training CNNs for regression prediction with Keras, but you’ll also have a Python code template you can follow for your own projects. Keras, Regression, and CNNs Project structure
Utiliser des images sur Internet : quelles sont les règles à respecter ? Vous cherchez des images pour illustrer vos contenus ou vos publicités ? La vigilance est de mise : des règles strictes s’appliquent. Elles dépendent du type d’image, de son utilisation, et des contenus visibles sur l’image. Pour les vidéos, les principes sont similaires. Deux types d’image : créative ou éditoriale La première règle à respecter est de sourcer l’image. Il convient ensuite de déterminer le type de l’image. Il est important de déterminer le type d’image car les règles relatives aux droits diffèrent. Deux types d’usage : éditorial ou commercial (promotionnel) S’il convient de dissocier les images éditoriales et créatives, il est également nécessaire de dissocier deux usages : l’usage éditorial et l’usage commercial. Ainsi, “les personnes prises en photographie dans le domaine public ne peuvent s’opposer à une publication dans la presse”. En revanche, si l’usage est commercial, cette autorisation est nécessaire – tout comme celle des marques et des lieux reconnaissables.
CNN Long Short-Term Memory Networks Last Updated on August 14, 2019 Gentle introduction to CNN LSTM recurrent neural networks with example Python code. Input with spatial structure, like images, cannot be modeled easily with the standard Vanilla LSTM. The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos. In this post, you will discover the CNN LSTM architecture for sequence prediction. After completing this post, you will know: About the development of the CNN LSTM model architecture for sequence prediction.Examples of the types of problems to which the CNN LSTM model is suited.How to implement the CNN LSTM architecture in Python with Keras. Discover how to develop LSTMs such as stacked, bidirectional, CNN-LSTM, Encoder-Decoder seq2seq and more in my new book, with 14 step-by-step tutorials and full code. Let’s get started. CNN LSTM Architecture — Show and Tell: A Neural Image Caption Generator, 2015.