We presented this work at the Facebook f8 conference. You can see this video of our talk here, or read on for more details and examples. Generative models are models that generate music, images, text, and other complex data types. In recent years generative models have advanced at an astonishing rate, largely due to deep learning, and particularly due to generative adversarial models GANs. However, GANs are notoriously difficult to train, due to requiring a large amount of data, needing many GPUs and a lot of time to train, and being highly sensitive to minor hyperparameter changes.

Look at data

Transfer learning refers to pre-training a model using readily available data and quick and easy to calculate loss functions, and then fine-tuning that model for a task that may have fewer labels, or be more expensive to compute.

This seemed like a potential solution to the GAN training problem, so in late fast. The pre-trained model that fast. A simple fast loss function was initially used: mean squared pixel error. This U-Net could be trained in just a few minutes.

Then, the loss function was replaced was a combination of other loss functions used in the generative modeling literature more details in the f8 video and trained for another couple of hours.

The plan was then to finally add a GAN for the last few epochs - however it turned out that the results were so good that fast. DeOldify was developed at around the same time that fast.

Jason Antic watched the Spring fast. His ambition was to be able to successfully colorize real world old images with the noise, contrast, and brightness problems caused by film degradation.

The model needed to be trained on photos with these problems simulated. Jeremy saw some of the early results and was excited to see that someone else was getting great results in image generation. He reached out to Jason to learn more.

restoration gains with GANs

Jeremy and Jason soon realized that they were both using very similar techniques, but had both developed in some different directions too. So they decided to join forces and develop a decrappification process that included all of their best ideas.

The result of joining forces was a process that allowed GANs to be skipped entirely, and which can be trained on a gaming pc.

In fact, it probably encouraged it. Jason then took the process even further, moving from still images to movies. He discovered that just a tiny bit of GAN fine-tuning on top of the process developed with fast. The advanced microscopes at the WABC are heavily used by researchers at the Salk as well as several neighboring institutions including Scripps and UCSD to investigate the ultrastructural organization and dynamics of life, ranging anywhere from carbon capturing machines in plant tissues to synaptic connections in brain circuits to energy generating mitochondria in cancer cells and neurons.

The scanning electron microscope is distinguished by its ability to serially slice and image an entire block of tissue, resulting in a 3-dimensional volumetric dataset at nearly nanometer resolution. Upon meeting Uri, it was immediately clear that fast.In this tutorial, we'll see how the same API allows you to get a look at the inputs and outputs of your model, whether in the vision, text or tabular application.

To quickly get access to all the vision functions inside fastai, we use the usual import statements. It's set up with an imagenet structure so we use it to load our training and validation datasets, then label, transform, convert them into ImageDataBunch and finally, normalize them. Once your data is properly set up in a DataBunchwe can call data. Note that the images were automatically de-normalized before being showed with their labels inferred from the names of the folder.

We can specify a number of rows if the default of 5 is too big, and we can also limit the size of the figure. Now let's create a Learner object to train a classifier. Since the validation set is usually sorted, we get only images belonging to the same class.

We can then again specify a number of rows, a figure size, but also the dataset on which we want to make predictions. Now let's try these on the planet dataset, which is a little bit different in the sense that each image can have multiple tags and not just one label. Here each images is labelled in a file named 'labels. We have to add 'train' as a prefix to the filenames, '. Then we can then create a Learner object pretty easily and train it for a little bit. For the next example, we are going to use the BIWI head pose dataset.

On pictures of persons, we have to find the center of their face. For the fastai docs, we have built a small subsample of the dataset images and prepared a dictionary for the correspondance filename to center. To grab our data, we use this dictionary to label our items. We also use the PointsItemList class to have the targets be of type ImagePoints which will make sure the data augmentation is properly applied to them. Nav GitHub News.

Look at data. Beginner's tutorial, explains how to quickly look at your data or model predictions.The concept is that we train two models at the same time: a generator and a critic.

The generator will try to make new images similar to the ones in a dataset, and the critic will try to classify real images from the ones the generator does.

fastai gan example

The generator returns images, the critic a single number usually a probability, 0. This is just a shell to contain the two models. Resnet block of nf features. In generator mode, this loss function expects the output of the generator and some target a batch of real images. This loss function has the following signature. AdaptiveLoss crit :: Module.

Machine Learning In Just 5 Lines Of Code: Fast.ai’s New Release

Expand the target to match the output size before applying crit. Warning: The GANTrainer is useless on its own, you need to complete it with one of the following switchers.

Nav GitHub News. Basic support for Generative Adversarial Networks. We train them against each other in the sense that at each step more or lesswe: Freeze the generator and train the critic for one step by: getting one batch of true images let's call that real generating one batch of fake images let's call that fake have the critic evaluate each batch and compute a loss function from that; the important part is that it rewards positively the detection of real images and penalizes the fake ones update the weights of the critic with the gradients of this loss Freeze the critic and train the generator for one step by: generating one batch of fake images evaluate the critic on it return a loss that rewards positively the critic thinking those are real images update the weights of the generator with the gradients of this loss.

Wrapping the modules. Callbacks for GAN training. GAN data. GAN Learner.Here is a quick example to explain:. All future downloads occur at the paths defined in the config file based on the type of download.

Please note that it is possible to update the default path locations in the config file. Let's first create a backup of the config file, then update the config to show the changes and re update the new config with the backup file. Now let's remove any updates we made to Config file that we made for the purpose of this example. This function can be used to download any file from the internet to a location passed by dest argument of the function.

It should not be confused, that this function can only be used to download fastai-files. That couldn't be further away from the truth.

As an example, let's download the pets dataset from the actual source file:. As can be seen, the file has been downloaded to the local path provided in dest argument. Calling the function again doesn't trigger a download since the file is already there. This can be confirmed by checking that the last modified time of the file that is downloaded doesn't get updated. Otherwise, it's only when the file doesn't exists that the download is triggered.

Extract fname to dest using tarfile or zipfile. Download url to fname if dest doesn't exist, and un-tgz or unzip to folder dest. The url can be a default url from the URLs class or a custom url. This convenience function extracts the downloaded files to dest by default.

fastai gan example

If files are already available at the fname location but not available then a symbolic link is created for each file from fname location to dest. Nav GitHub News. External data. The dataset contains over 15K images of 20 people 6 females and 14 males - 4 people were recorded twice. For each frame, a depth image, the corresponding rgb image both x pixelsand the annotation is provided.

CIFAR : The famous cifar dataset which consists of 32x32 colour images in 10 classes, with images per class. Useful for experimenting with Language Models. About 40 to images per category.

Most categories have about 50 images. PETS : A 37 category pet dataset with roughly images for each class. The dataset contains 30, training and 1, testing examples for each class. Additional labels include name of individual making the vocalization and its age. PETS path. The config is now available as config.

Let's confirm that the file was indeed downloaded correctly. Sometimes the extracted folder does not have the same name as the downloaded file.This article describes the techniques and training a deep learning model for image improvement, image restoration, inpainting and super resolution. This utilises many techniques taught in the Fastai course and makes use of the Fastai software library.

Super resolution is the process of upscaling and or improving the details within an image. Often a low resolution image is taken as an input and the same image is upscaled to a higher resolution, which is the output.

The details in the high resolution output are filled in where the details are essentially unknown. Super resolution is essentially what you see in films and series like CSI where someone zooms into an image and it improves in quality and the details just appear. Little did I think that less than a year on I would be training my own super resolution model and writing about it.

This is part of a series of articles I am writing as part of my ongoing learning and research in Artificial Intelligence and Machine Learning.

PyTorch in 5 Minutes

These topics and techniques have been quite challenging to understand and its taken me many months to experiment and write this. Below is an example of a low resolution image with super resolution performed upon it to improve it:.

The problem deep machine learning based super resolution is trying to solve is that traditional algorithm based upscaling methods lack fine detail and cannot remove defects and compression artifacts.

For humans who carry out these tasks manually it is a very slow and painstaking process. The benefits are gaining a higher quality image from one where that never existed or has been lost, this could be beneficial in many areas or even life saving on medical applications.

Another use case is for compression in transfer between computer networks. Imagine if you only had to send a x pixel image where a x pixel image is needed. In the set of images below there are five images:. To accomplish this a mathematical function takes the low resolution image that lacks details and hallucinates the details and features onto it. In doing so the function finds detail potentially never recorded by the original camera.

There are potential ethical concerns with this mentioned at the end of this article, once how the model and its training is explained. Models that are trained for super resolution should also be useful for repairing defects in a image jpeg compression, tears, folds and other damage as the model has a concept of what certain features should look like, for example materials, fur or even an eye.

Image inpainting is the process of retouching an image to remove unwanted elements in the image, such as a wire fence. For training it is common to cut out sections of the image and train the model to replace the missing parts based on prior knowledge of what should be there.

Image inpainting is a usually a very slow process when carried out manually by a skilled human. Super resolution and inpainting seem to be often regarded as separate and different tasks. This assumes those defects and gaps exist in the training data for their restoration to be learnt by the model. One of the limitations of GANs is that they are effectively a lazy approach as their loss function, the critic, is trained as part of the process and not specifically engineered for this purpose.

This could be one of the reasons many models are only good at super resolution and not image repair. For example a model trained for the super resolution of animals may not be good for the super resolution of human faces. The model trained with the methods detailed in this article seemed to perform well across varied dataset including human features, indicating a universal model that is effective at upscaling on any category of image may be possible.

Following are ten examples of X2 super resolution doubling the image size from the same model trained on the Div2K dataset, high resolution images of a variety of subject matter categories.In short, to facilitate hassle-free deep learning solutions. The libraries leverage the dynamism of the underlying Python language and the flexibility of the PyTorch library.

Using fastgpuone can check for scripts to run, and then run them on the first available GPU. If no GPUs are available, it waits. This property allows researchers to run ablation studies by taking advantage of all GPUs with no parallel processing overhead or manual intervention.

Using fastcore one can add features such as multiple dispatches from Julia, mixins from Ruby and more to Python. Using argparse in Python creating a quick script for the command-line argument can be tedious.

Now coming to the part where fastai allows newcomers to try computer vision, NLP, recommendation system algorithms with almost similar looking code under 5 lines.

fastai gan example

This dataset contains 7, images of cats and dogs from 37 different breeds. Here, a pre-trained model trained on 1.

This example demonstrates how to train a model to identify user sentiment about a movie from a review. Using MovieLens dataset, this example shows how to build a recommender system that tells the kind of movies people might like, based on their previous viewing habits.

Know more about fastai v2 here. I have a master's degree in Robotics and I write about machine learning advancements. What Do You Think? What's Your Reaction? In Love. Not Sure. Ram Sagar I have a master's degree in Robotics and I write about machine learning advancements. Karnataka Govt. Who we are.The fastai library simplifies training fast and accurate neural nets using modern best practices. It's based on research in to deep learning best practices undertaken at fast.

If you're looking for the source code, head over to the fastai repo on GitHub. For brief examples, see the examples folder; detailed examples are provided in the full documentation see the sidebar. For instance, here is the notebook source of what you're reading now. For troubleshooting, and alternative installations including pip and CPU-only options see the fastai readme. To get started quickly, click Applications on the sidebar, and then choose the application you're interested in.

That will take you to a walk-through of training a model of that type. You can then either explore the various links from there, or dive more deeply into the various fastai modules.

We've provided below a quick summary of the key modules in this library. For details on each one, use the sidebar to find the module you're interested in. Each module includes an overview and example of how to use it, along with documentation for every class, function, and method. API documentation looks, for example, like this:.

Types for each parameter, and the return type, are displayed following standard Python type hint syntax. Sometimes for compound types we use type variables. Types that are defined by fastai or Pytorch link directly to more information about that type; try clicking Image in the function above for an example. The docstring for the symbol is shown immediately after the signature, along with a link to the source code for the symbol in GitHub.

After the basic signature and docstring you'll find examples and additional details not shown in this example. As you'll see at the top of the page, all symbols documented like this also appear in the table of contents. For inherited classes and some types of decorated function, the base class or decorator type will also be shown at the end of the signature, delimited by For vision.


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