Quick Start : The easiest way to colorize images using DeOldify for free! The most advanced version of DeOldify image colorization is available here, exclusively. Try a few images for free! MyHeritiage In Color. Image artistic Video. NEW Having trouble with the default image colorizer, aka "artistic"? Try the "stable" one below. It generally won't produce colors that are as interesting as "artistic", but the glitches are noticeably reduced.
Image stable. Instructions on how to use the Colabs above have been kindly provided in video tutorial form by Old Ireland in Colour's John Breslin. It's great!
Towards Data Science
Click video image below to watch. Get more updates on Twitter. Simply put, the mission of this project is to colorize and restore old images and film footage. We'll get into the details in a bit, but first let's see some pretty pictures and videos!Influvac 2019 abbott
Lemuel Smith and their younger children in their farm house, Carroll County, Georgia. Note: What you might be wondering is while this render looks cool, are the colors accurate? The original photo certainly makes it look like the towers of the bridge could be white.
We looked into this and it turns out the answer is no - the towers were already covered in red primer by this time. So that's something to keep in mind- historical accuracy remains a huge challenge!
NoGAN training is crucial to getting the kind of stable and colorful images seen in this iteration of DeOldify. NoGAN training combines the benefits of GAN training wonderful colorization while eliminating the nasty side effects like flickering objects in video.
Believe it or not, video is rendered using isolated image generation without any sort of temporal modeling tacked on. Then, as with still image colorization, we "DeOldify" individual frames before rebuilding the video. In addition to improved video stability, there is an interesting thing going on here worth mentioning. It turns out the models I run, even different ones and with different training structures, keep arriving at more or less the same solution.
That's even the case for the colorization of things you may think would be arbitrary and unknowable, like the color of clothing, cars, and even special effects as seen in "Metropolis". My best guess is that the models are learning some interesting rules about how to colorize based on subtle cues present in the black and white images that I certainly wouldn't expect to exist.
This result leads to nicely deterministic and consistent results, and that means you don't have track model colorization decisions because they're not arbitrary. Additionally, they seem remarkably robust so that even in moving scenes the renders are very consistent. Other ways to stabilize video add up as well. This stands to reason because the model has higher fidelity image information to work with and will have a greater chance of making the "right" decision consistently.
Closely related to this is the use of resnet instead of resnet34 as the backbone of the generator- objects are detected more consistently and correctly with this. This is especially important for getting good, consistent skin rendering. It can be particularly visually jarring if you wind up with "zombie hands", for example. Additionally, gaussian noise augmentation during training appears to help but at this point the conclusions as to just how much are bit more tenuous I just haven't formally measured this yet.
Instead, most of the training time is spent pretraining the generator and critic separately with more straight-forward, fast and reliable conventional methods. A key insight here is that those more "conventional" methods generally get you most of the results you need, and that GANs can be used to close the gap on realism. During the very short amount of actual GAN training the generator not only gets the full realistic colorization capabilities that used to take days of progressively resized GAN training, but it also doesn't accrue nearly as much of the artifacts and other ugly baggage of GANs.Colorizing black and white images with deep learning has become an impressive showcase for the real-world application of neural networks in our lives.
Jason Antic decided to push the state-of-the-art in colorization with neural networks a step further. His recent DeOldify deep learning project not only colorizes images but also restores them, with stunning results:. You can get impressive results on video as well he developed a new technique called NoGAN to raise the bar on movie colorization :. Jason is a software engineer at Arrowhead General Insurance Agency, where he focuses on automation and quality assurance across the tech stack.
Needless to say, I was psyched to chat with Jason for this Humans of Machine Learning humansofml interview. I have never restored a photo before this project. My experience with Photoshop is basically a little cropping here and there, and randomly adding effects — like how a kid mixes all the sodas together. I had to do it with Photoshop in high school photography class.
It was absolutely painstaking, but oddly satisfying at the same time. Did you have any special photos from your family or elsewhere that you wanted to restore? Oh sure I do! I suspected this would be the case, but the response has been rather extraordinary on this.
For a while, even before I got heavily into deep learning, I thought the whole concept of automatically colorizing old black and white photos was just a cool idea.
But it never seemed to be done very well, even with the existing deep learning models. As I was taking the fast.
Decrappification, DeOldification, and Super Resolution
The most immediately obvious and wrong! But it still comes across as quite hacky to me to do it this way. So it just seemed like a no-brainer to go the GAN route to solve the problem of realistic colorization!
I do feel a little funny now about how I just went ahead and used the two terms in my project description without being careful about definitions first. But simply put, colorization in my mind for this project is strictly just taking the photos from monochrome to a believable coloring, regardless of the flaws of the image like fading and whatnot. I really do consider this an art. Are those two separate processes in your model, or do they happen at the same time?
For example, this is counterintuitive but I wound up getting much better results when I stopped trying to get the critic to evaluate everything at once.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch. With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead.
Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autogradautogradChaineretc. While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.
You get the best of speed and flexibility for your crazy research. It is built to be deeply integrated into Python. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba.
Our goal is to not reinvent the wheel where appropriate. PyTorch is designed to be intuitive, linear in thought and easy to use. When you execute a line of code, it gets executed.
There isn't an asynchronous view of the world. When you drop into a debugger, or receive error messages and stack traces, understanding them is straightforward.
The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.
PyTorch has minimal framework overhead. The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient.
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Already on GitHub? Sign in to your account. I run this code on Colab Running torch. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! Looks like it's because of the way Self Attention is implemented in fastai, and Spectral Normalization.
Weight though the model may not train and perform as well as with the default settings. You can simply strip Spectral Normalization from a fully trained model just before exporting to Onnx to get rid of this error. I just ran my model through this code I hacked together:.
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Sign in to comment. Linked pull requests. You signed in with another tab or window.Simply put, the mission of this project is to colorize and restore old images. I'll get into the details in a bit, but first let's get to the pictures! Petersburg, Russia, Woman relaxing in her livingroomSweden. Medical Students pose with a cadaver around Interior of Miller and Shoemaker Soda Fountain, Edinburgh from the sky in the s. People watching a television set for the first time at Waterloo station, London, Portsmouth Square in San Franscisco, This is a deep learning based model.
Except the generator is a pretrained Unetand I've just modified it to have the spectral normalization and self-attention. It's a pretty straightforward translation. I'll tell you what though — it made all the difference when I switched to this after trying desperately to get a Wasserstein GAN version to work. I liked the theory of Wasserstein GANs but it just didn't pan out in practice. The difference here is the number of layers remains constant — I just changed the size of the input progressively and adjusted learning rates to make sure that the transitions between sizes happened successfully.
It seems to have the same basic end result — training is faster, more stable, and generalizes better. The second is the loss score from the critic. For the curious — Perceptual Loss isn't sufficient by itself to produce good results. Key thing to realize here is that GANs essentially are learning the loss function for you — which is really one big step closer to toward the ideal that we're shooting for in machine learning. And of course you generally get much better results when you get the machine to learn something you were previously hand coding.
That's certainly the case here. The beauty of this model is that it should be generally useful for all sorts of image modification, and it should do it quite well. What you're seeing above are the results of the colorization model, but that's just one component in a pipeline that I'm looking to develop here with the exact same model.
What I develop next with this model will be based on trying to solve the problem of making these old images look great, so the next item on the agenda for me is the "defade" model.
I've committed initial efforts on that and it's in the early stages of training as I write this. I've already seen some promissing results on that as well:.
So that's the gist of this project — I'm looking to make old photos look reeeeaaally good with GANs, and more importantly, make the project useful. And yes, I'm definitely interested in doing video, but first I need to sort out how to get this model under control with memory it's a beast. It'd be nice if the models didn't take two to three days to train on a TI as well typical of GANs, unfortunately.
In the meantime though this is going to be my baby and I'll be actively updating and improving the code over the foreseable future. I'll try to make this as user-friendly as possible, but I'm sure there's going to be hiccups along the way. Oh and I swear I'll document the code properly Admittedly I'm one of those people who believes in "self documenting code" LOL. This project is built around the wonderful Fast.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Get more updates on Twitter. Simply put, the mission of this project is to colorize and restore old images. I'll get into the details in a bit, but first let's get to the pictures! BTW — most of these source images originally came from the TheWayWeWere subreddit, so credit to them for finding such great photos.
Petersburg, Russia, This is a deep learning based model. More specifically, what I've done is combined the following approaches:. The beauty of this model is that it should be generally useful for all sorts of image modification, and it should do it quite well.
What you're seeing above are the results of the colorization model, but that's just one component in a pipeline that I'm looking to develop here with the exact same model.
What I develop next with this model will be based on trying to solve the problem of making these old images look great, so the next item on the agenda for me is the "defade" model. I've committed initial efforts on that and it's in the early stages of training as I write this. I've already seen some promising results on that as well:.
So that's the gist of this project — I'm looking to make old photos look reeeeaaally good with GANs, and more importantly, make the project useful. And yes, I'm definitely interested in doing video, but first I need to sort out how to get this model under control with memory it's a beast. It'd be nice if the models didn't take two to three days to train on a TI as well typical of GANs, unfortunately. In the meantime though this is going to be my baby and I'll be actively updating and improving the code over the foreseeable future.
I'll try to make this as user-friendly as possible, but I'm sure there's going to be hiccups along the way. Oh and I swear I'll document the code properly Admittedly I'm one of those people who believes in "self documenting code" LOL.
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