Ссылка на файл: >>>>>> http://file-portal.ru/Pix2pix рисуем ужасные рисунки и оживляем их/
Нейросеть превращает наброски лиц в жуткие портреты
Нейросеть превращает рисунки в фотореалистичные портреты
Video: ОНО РЕАЛЬНО! КАК ОНО СЮДА ПОПАЛО?
This tutorial will guide you on how to use the pix2pix software for learning image transformation functions between parallel datasets of corresponding image pairs. As we see above, the output is not identical to the input, but the network does a fairly decent job. Many important image processing tasks can be framed as image to image translation tasks of this sort. Deblurring or denoising images can be framed in this way, and indeed there had been a great deal of past research in learning various specific image-to-image translation tasks like those and others. The nice thing about pix2pix is that it is generic; it does not require pre-defining the relationship between the two types of images. It makes no assumptions about the relationship and instead learns the objective during training, by comparing the defined inputs and outputs during training, and inferring the objective. This makes pix2pix highly flexible and adaptable to a wide variety of situations, including ones where it is not easy to verbally or explicitly define the task we want to model. We get a good sense of this by considering a more complicated example, that of the CMP facades dataset which pix2pix has a download link for. We have on one side photographs of building facades, and on the other side, their corresponding label maps which have been hand-labeled by human beings. We can train pix2pix to generate the facade pictures from the label maps. If we do that, we can then attempt to generate an output from a test input, and compare the output to the original target image in the test dataset. The output has numerous features that are different from the target, and has a somewhat different color, and is especially guessing in the regions where the labels are sparse. But it gets most of the main architectural features in the right place. This leads us to believe that we can mock up whole new label maps and create realistic looking facades from them! This could be very useful for an architect; they can sketch a design for a building and then quickly prototype textures for it perhaps choosing from several dozen since they are so easy to produce. Of course, we could have trained the network in the other direction as well; train it to generate the label maps from the real images. This can be very useful as well; suppose you are in charge of a startup which is generating special types of label maps from satellite images. This provides a way to do it quickly and cheaply. Another important quality of pix2pix is that it requires a relatively small number of examples — for a low-complexity task, perhaps only samples, and usually less than , in contrast to networks which often requires tens or even hundreds of thousands of samples. The downside is that the model loses some generality, perhaps overfitting to the training samples, and thus can feel a bit repetitive or patchy. Still, those practical advantages make it extremely easy to deploy for a wide variety of images, enabling rapid experimentation. The beauty about a trained pix2pix network is that it will generate an output from any arbitrary input. By training a pix2pix network to convert the sketch into the image, we are then able to use the model to convert a new unseen sketch into an image of the object type. Additionally, he wrote a very good description of what pix2pix does , as well as the tensorflow implementation of pix2pix that the practical part of this guide will use later. Image-to-Image Tensorflow Demo https: The project was completed in the same week that the original paper and repository was released, demonstrating how quickly one can create an applied work from it. In Invisible Cities, a collection of map tiles and their corrsponding satellite images from multiple cities were downloaded from the MapBox API. A pix2pix model was trained to convert the map tiles into the satellite images. After training the Venice model, we take a map tile from a different city, Milan, Italy, and run it through the Venice pix2pix generator. More examples can be found in the gallery of the main project page. In Neural City , Jaspaer van Loenen trained pix2pix on Google streetview images to convert depth maps into street view photos. First try at generating streetview-like cityscapes deeplearning pix2pix pic. Another clever dataset to work with are images of faces matched to their detected facetracker landmarks. By training pix2pix to convert the facetracker representation to the original images of faces, you can generate faces from an arbitrary set of landmarks. See the following by Mario Klingemann who trained pix2pix to generate face sketches trained on sketches found in the dataset from the British Library. Generating faces from a sketch. I trained a pix2pix net on bldigital faces. Left input, right output. The real-time application was run live for a workshop. The result let him create generative versions of himself in the pose of Ray Kurzweil on stage. Full person-to-person image translation with machine learning! Bertrand Gondouin trained pix2pix to turn sketches of pokemon into actual pokemons in a live drawing interface. Sketches done using a graphics tablet, colored by a a real-time neural network trained unsupervised on Pokemon pictures x2 pix2pix , AI pic. As did Memo Akten , training a network to convert Canny edge detections from a webcam to generate images trained from a dataset containing the collections of museums. Memo also released his code for running the webcam demo. GAN trained on art collections of museums watching me draw As a follow-up to Invisible Cities , I made a drawing interface with p5. The following is a tutorial for how to use the tensorflow version of pix2pix. If you wish to, you can also use the original torch-based version or a newer pytorch version which also contains a CycleGAN implementation in it as well. Although these instructions are for the tensorflow version, they should be fairly relevant to the others with just minor modifications in syntax. You should be able to use any of the versions and get similar results. In using pix2pix, there are two modes. The first is training a model from a dataset of known samples, and the second is testing the model by generating new transformations from previously unseen samples. Training pix2pix means creating, learning the parameters for, and saving the neural network which will convert an image of type X into an image of type Y. An example of this would be converting images of lines into satellite photographs. This is useful because it allows us to generate sophisticated and detailed imagery from quick and minimal representations. The reverse is possible as well; to train a network to convert the real imagery into its corresponding symbolic form. This can be useful for many practical tasks; for example automatically finding and labeling roads and infrastructure in satellite images. Once a model has been generated, we use testing mode to output new samples. In order to run the software on your machine, you need to have an NVIDIA GPU which is supported by CUDA. Here is a list of supported devices. At least 2GB of VRAM are recommended, although realistically, with less than say 4GB, you may have to produce smaller-sized samples. If you have an older laptop, consider using a cloud-based platform instead todo: Once you have successfully run the installer for CUDA, you can find it on your system in the following locations:. The installer should do this automatically. It is not required to run Tensorflow but is highly recommended, as it makes many of the programs much more resource-efficient, and is probably required for pix2pix. You have to register first with NVIDIA easy to get access to the download. You should download the latest version for your platform unless you have an older version of CUDA. At time of this writing, cuDNN 5. Copy these files into those same folders inside of where you have CUDA installed see step 1. In most cases this can be done with pip. Clone or download the above library. It is possible to do all of this with the original torch-based pix2pix in which case you have to install torch instead of tensorflow for step 3. These instructions will assume the tensorflow version. First we need to prepare our dataset. The X and Y image each occupy half of the full image in the set. Thus they are the same size. It does not matter which order they are placed into, as you will define the direction in the training command just remember which way because you need to be consistent. Additionally, by default the images are assumed to be square if they are not, they will be squashed into square input and output pairs. AtoB means train the model such that the left half of your training images is the input and the right half is the output generated. BtoA is the reverse. In practice, more is usually better, but pix2pix may stop learning after a small number of epochs, in which case it takes longer than you need. It is also sometimes possible to train it too much, as if to overcook it, and get distorted generations. The loss function does not necessarily correspond well to quality of images generated although there is recent research which does create a better equivalency between them. Note also that the order in which parameters are written in the command does not actually matter. Additionally, there are optional parameters which may be useful:. This can be useful for running for a while and checking to see quality, and then resuming training for longer if you are unsatisfied. There are more advanced options, which you can see in the arguments list in pix2pix. The adventurous may wish to experiment with these as well. Unfortunately, pix2pix-tensorflow does not currently allow you to change the actual size of the produced samples and is hardcoded to have a height of px. Simply changing it in pix2pix. If you wish to generate bigger samples, you can do so using the original torch-based pix2pix which does have it as a command line parameter, or more adventurously adapt the tensorflow code to arbitrarily sized samples. This will definitely be true if your original images are smaller than the desired size because subpixel details are not available in the training data, but even if your data is sized sufficiently, it may still occur. Worth trying out, but your results may vary. If you trained AtoB for example, it means providing new images of A and getting out hallucinated versions of it in B style. In pix2pix, testing mode is still setup to take image pairs like in training mode, where there is an X and a Y. This is because a useful thing to do is to hold out a test or validation set of images from training, then generate from those so you can compare the constructed Y to the known Y, as a way to visually evaluate the data. Several of the downloadable datasets e. Once your testing data has been prepared, run the following command again from the root folder of pix2pix-tensorflow:. Skip Navigation ml4a guides demos classes code. Pix2Pix This tutorial will guide you on how to use the pix2pix software for learning image transformation functions between parallel datasets of corresponding image pairs. What does pix2pix do? Handdrawn input generating Venice satellite image.
Магазин одежды оазис иркутск каталог
Медицинское освидетельствование тесты
Леново s860 не видит компьютер что делать
Нейросеть «оживляет» рисунки котов. Выглядит жутко
Почему быстро пьянею от пива
Где снимали фильм бриллиантовая
Инструкция оформления больничного листа
Нейросеть научилась рисовать жуткие портреты по наброскам
Где переночевать по дороге на юг
Схема перевода денежных средств
Pix2Pix
История компьютерной техники
Расчет электроэнергии по нормативам
Как сделать ваз 2107 глушитель
Алгоритм превращает рисунки в «реалистичных» людей, и это ваш худший ночной кошмар
Образец бланк должностной инструкции