Among photographers, it is known that on "big" cameras the use of 14-bit readout compared to 12-bit can have a positive impact on shadow detail. How does this apply to small sensors in smartphone cameras?
InvokeAI 2.2 is now available to everyone. This update brings in exciting features, like UI Outpainting, Embedding Management and more. See highlighted updates below, or the full release notes for everything included in the release.
Great for seamless patterns, abstract drawings, and watercolor-styled images. How to use it and train a neural network on your own pictures?
Download the model here: https://huggingface.co/netsvetaev/netsvetaev-free
The InvokeAI team is excited to share our latest feature release, with a set of new features, UI enhancements, and CLI capabilities.
The present invention relates to an analog signal capturing devices generally and monochrome or color image capture sensors, such as a scanner or a Charge-Coupled-Device (“CCD”) for video and photo camera in particular, which are almost free from moiré and aliasing. The present invention relates to methods for enhancing the resolution of an image capture device and device for digital color/grey image displaying also.
Multimodality has led the pack in machine learning in 2021. Neural networks are wolfing down images, text, speech and music all at the same time. OpenAI is, as usual, top dog, but as if in defiance of their name, they are in no hurry to share their models openly. At the beginning of the year, the company presented the DALL-E neural network, which generates 256x256 pixel images in answer to a written request. Descriptions of it can be found as articles on arXiv and examples on their blog.
As soon as DALL-E flushed out of the bushes, Chinese researchers got on its tail. Their open-source CogView neural network does the same trick of generating images from text. But what about here in Russia? One might say that “investigate, master, and train” is our engineering motto. Well, we caught the scent, and today we can say that we created from scratch a complete pipeline for generating images from descriptive textual input written in Russian.
In this article we present the ruDALL-E XL model, an open-source text-to-image transformer with 1.3 billion parameters as well as ruDALL-E XXL model, an text-to-image transformer with 12.0 billion parameters which is available in DataHub SberCloud, and several other satellite models.
Every time when the essential question arises, whether to upgrade the cards in the server room or not, I look through similar articles and watch such videos.
Channel with the aforementioned video is very underestimated, but the author does not deal with ML. In general, when analyzing comparisons of accelerators for ML, several things usually catch your eye:
The answer to the question "which card is better?" is not rocket science: Cards of the 20* series didn't get much popularity, while the 1080 Ti from Avito (Russian craigslist) still are very attractive (and, oddly enough, don't get cheaper, probably for this reason).
All this is fine and dandy and the standard benchmarks are unlikely to lie too much, but recently I learned about the existence of Multi-Instance-GPU technology for A100 video cards and native support for TF32 for Ampere devices and I got the idea to share my experience of the real testing cards on the Ampere architecture (3090 and A100). In this short note, I will try to answer the questions:
It would seem that the question of the color of the Moon and the Sun from space for modern science is so simple that in our century there should be no problem at all with the answer. We are talking about colors when observing precisely from space, since the atmosphere causes a color change due to Rayleigh light scattering. «Surely somewhere in the encyclopedia about this in detail, in numbers it has long been written,» you will say. Well, now try searching the Internet for information about it. Happened? Most likely no. The maximum that you will find is a couple of words about the fact that the Moon has a brownish tint, and the Sun is reddish. But you will not find information about whether these tints are visible to the human eye or not, especially the meanings of colors in RGB or at least color temperatures. But you will find a bunch of photos and videos where the Moon from space is absolutely gray, mostly in photos of the American Apollo program, and where the Sun from space is depicted white and even blue.
Manual lung segmentation takes about 10 minutes and it requires a certain skill to get the same high-quality result as with automatic segmentation. Automatic segmentation takes about 15 seconds.
I assumed that without a neural network it would be possible to get an accuracy of no more than 70%. I also assumed, that morphological operations are only the preparation of an image for more complex algorithms. But as a result of processing of those, although few, 40 samples of tomographic data on hand, the algorithm segmented the lungs without errors. Moreover, after testing in the first five cases, the algorithm didn’t change significantly and correctly worked on the other 35 studies without changing the settings.
Also, neural networks have a disadvantage — for their training we need hundreds of training samples of lungs, which need to be marked up manually.
