Topaz Video Ai Model
Hebe Zuelke
Wow, I really expect more from Topaz Labs, maybe something like we know or we will investigate, etc. I have read other post from people saying the same thing. The main programs download with over 200mbps, but all of the models are under 6mbps.
Here are the scripts for anyone wanting to download all models for offline usage. To use them open the terminal from inside the app and run the script. Using a terminal opened from outside the app will not work. Feel free to modify the scripts depending on the models and input resolutions you need. Feel free to ask any questions below.
Hello @suraj , when will we really be entitled to a model manager like in version 2.6.4? You do not provide any answer to this question which is asked by several people among us here that they ask for a return from the model manager so that we can choose our own models that we want and to include the old models artemis, gaia, etc. Thank you for providing us with a clear and clear answer on this question.
Is there a reason why it uses ffmpeg instead of something that just copies (maybe ftp based or http based)? eg. is there a common ftp copy or http copy command that would work better (while still being fast)?
I never have any downloading issues for models I have used before, and when updating to the latest version of TVAI, I always choose not to delete anything so whatever I have will stay in the models folder. I also ensure that I am offline before starting any process that I am in a hurry to complete.
P.S. In the absence of better model management tool it would be very nice of Topaz Labs if they could provide list of all model files for each release along with their size and hash info. That way there would be less question marks is something missing after the download and did any corruptions occur during the download.
So generally I use the method of looking in the Video AI log file for the files that it failed on (that have the failed message on) and downloading those in a web browser and saving them to the models folder.
I run Topaz Video AI and select a video and select the things I want done using the AI models I specify (such as changing the scale with a particular model or increasing the frame rate with one etc.). If it fails (eg. waits for ages on one or more or gives the error message about it) I open the log folder (help->logging->open log folder) then exit the app so that I can open any log file (since it might prevent you if the app has the one you want open) and open the last log file in notepad.
Thank you for your prompt and thorough reply. Unfortunately I have ffmpeg crashes whenever I try to run the script and nothing shows up as failed in logs. I will have to ask developer what is going on.
I had exported a topaz model I trained (Job 13 - J13) as a group. After I import the model in another session, cryosparc looks for the files in folder J13 when topaz is launched. cryosparc also saves the preprocessed files in J13.
Is there a way for this to not require the J13 folder in a new/different sessions? Is there another way to reuse my own trained models?
To be able to use my own pre-trained model is a bit of a production.
This error appears when a model file needed to process an image is missing or the AI engine is not able to run on the computer. Please go through the sections below sequentially to troubleshoot this error.
If your computer graphics card does not meet the system requirements, you can open Topaz Photo AI and click the Processor menu to try the CPU option. If the CPU can process an image, that indicates the graphics card is does not meet the minimum requirements for Topaz Photo AI.
For situations where neither the CPU or graphics card can process the image, it's very likely that your installation of Topaz Photo AI is missing model files. This can happen if antivirus, firewall, or a VPN blocked the installer from downloading the files.
If that does not fix it, please open Topaz Photo AI and go to the menu bar on the top. Click the Help > Open Log Folder menu option, which will open up a folder with text files. The folder will look like this.
Email he...@topazlabs.com and attach ALL the text files in the folder to the email. Make the email subject "Error Loading Model in Topaz Photo AI" so our support team can quickly review and help you resolve the error.
The Topaz BSB cable accessory A-BSB1-2 (length: 6 feet) allows for virtual serial via USB connectivity with select Topaz pad models. For installation information, see the BSB Cable Installation Guide.
Video Enhance AI is the best solution to enhance videos in a variety of ways, including upscaling a low-resolution video up to 8K, performing a frame rate conversion on your video clips, or applying up to a 2000% slow-motion effect. We're also continually looking for new ways to help you enhance video quality using our growing collection of AI models.
As we've grown both the number of AI models and the versions of each model in Video Enhance AI, we've listened closely to the helpful feedback our users have provided. One item that stood out involves offering better management of these AI models. Some of our AI models have pretty large file sizes that can have a real-world impact on users, especially those who have limited internet bandwidth to download them or do not have enough hard drive space to locally store them. That's why we're excited to announce the AI Model Manager in Video Enhance AI v2.4.
Do you have a one-off low-resolution video that suffers from aliasing? The AI Model Manager allows you to control which models you want to download on the fly. You can use it to download the Artemis Aliased & Moire model, process your video using it, and remove it until the next time you need it.We're very excited about the opportunities that the AI Model Manager affords our users. We recently spoke with Taylor Bishop, our lead developer, to discuss why we built it and its meaning for our future app and AI model development. But, before we dive into that interview, we'd like you to check out this brief video tutorial illustrating how to use the AI Model Manager in Video Enhance AI.
Hi, everyone! My name is Taylor Bishop. I'm the lead developer on both Video Enhance AI and Gigapixel AI. My core focus is ensuring that we present users with the AI technology that underpins both applications in the easiest to use way possible while allowing for as much flexibility as makes sense for our end users. My ultimate goal is to have every user, no matter what type of image or video they're looking to enhance, be able to look at the result from our products and say "wow."
We've been exploring ways to serve our users best regarding AI model management within Video Enhance AI. We built the AI Model Manager to strike a balance between ease-of-use and flexibility for our users. We kept asking ourselves, "How do we deliver new models to users without overloading them with options? How do we let people experiment but not overload those that want 1-click functionality?"
The goal was to allow users to dig in, experiment, and decide where to store our growing library of AI models. We also want to provide the user with control over how much of each model will be stored locally without interrupting the core user experience.
We've been growing our R&D team in recent months and hope to be putting out new model versions at least once every 1-2 months. Our R&D team is constantly experimenting with bleeding-edge techniques in the machine learning field, and our giant pseudo-supercomputers are chugging away every day to train those models. Even if we haven't actively changed the guts of one of our models, our computers are constantly training them, allowing us to deliver that value quicker than before.
I recommend reading this thorough article to learn more about how we train our AI models to improve the objective quality of still images and videos. The article was written by Partha Acharjee, one of our brilliant research engineers. He explains how we use AI to apply intelligent noise reduction to still images.
That question plagues every person at least once in their lives and has personally kept me up at night on multiple occasions. Cookies & Cream or Chocolate Chip Cookie Dough? Ben & Jerry's Cookie Dough Core or Heath Bar Crunch? The older I've gotten, the more comfortable I've become with the notion that I may never have a definitive answer to this question. What is life but a series of questions we ask ourselves, often with very few solutions?
If you're as excited about the new AI Model Manager and want to try it out for yourself, download the free trial today! Also, don't forget to leave a comment below to share your thoughts on Video Enhance AI and the new AI Model Manager.
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Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.
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