DataBloomI created Kerod a pure tensorflow 2
implementation of object detection algorithms (Faster R-CNN, DeTr)
aiming production. It stands for Keras Object Detection.
It aims to build a clear, reusable, tested, simple and
documented codebase for tensorflow 2.X.
You’ll be able to train models on COCO, Pascal VOC just by
launching the notebooks.
Here a link of the project: https://github.com/EmGarr/kerod.
Hope it will helps!
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With NVIDIA CloudXR, users don’t need to be physically tethered to a high-performance computer to drive rich, immersive environments.
NVIDIA CloudXR Release 2.0 is now available. With NVIDIA CloudXR, users don’t need to be physically tethered to a high-performance computer to drive rich, immersive environments. The CloudXR SDK runs on NVIDIA servers located in the cloud, edge or on-premises, delivering the advanced graphics performance needed for wireless virtual, augmented or mixed reality environments — collectively known as XR.
This latest release includes new features that allow more client support for various devices, including Oculus Quest 2, HoloLens 2 Display, for AR streaming. And the new latency profiler helps providers and developers understand CloudXR latencies and performance.
Additional features include:
Companies that have access to 5G networks can use NVIDIA CloudXR to stream immersive environments from their on-prem data centers. Telcos, software makers and device manufacturers can use the high bandwidth and low latency of 5G signals to provide high framerate, low-latency immersive XR experiences to millions of customers in more locations than previously possible.
“Using NVIDIA CloudXR and NVIDIA vGPU we have built a number of on-prem, high-performance solutions for customers who require the highest quality streaming XR,” said Andy Bowker, co-founder and CEO at The Grid Factory. “The NVIDIA CloudXR SDK has become a lynchpin for our end-to-end solutions, allowing us to deliver the highest quality turnkey solutions, and allowing our customers in turn to focus on their work instead of worrying about the technology.”
“NVIDIA CloudXR is an exciting new technology that’s becoming available on all platforms and devices,” said Greg Demchak, Director of the iLAB at Bentley. “With CloudXR, our customers will be able to extend their iTwin (Digital Twin) solutions to AR, VR, and MR devices.”
NVIDIA CloudXR Release 2.0 is now available for early access users. If you are interested in applying for access to this private beta, apply at the NVIDIA CloudXR DevZone Page.
To learn more about The Grid Factory and their CloudXR deployments, sign up for the upcoming webinar taking place on February 18.
When photographers take long-exposure photos, they maximize the amount of light their camera sensors receive. The technique helps capture scenes like the night sky, but it introduces blurring in the final image, as in the example at right. It’s not too different from cryo-electron microscopy, or cryo-EM, which scientists use to study the structure of Read article >
The post Freeze the Day: How UCSF Researchers Clear Up Cryo-EM Images with GPUs appeared first on The Official NVIDIA Blog.
The journey to making the upcoming film Gods of Mars changed course dramatically once real-time rendering entered the picture. The movie, currently in production, features a mix of cinematic visual effects with live-action elements. The film crew had planned to make the movie primarily using real-life miniature figures. But they switched gears last year once Read article >
The post Out of This World Graphics: ‘Gods of Mars’ Come Alive with NVIDIA RTX Real-Time Rendering appeared first on The Official NVIDIA Blog.
I published a tutorial where I explain how to build AutoEncoders
with Python + Keras. In particular, in this video you’ll learn
how to chain encoder + decoder architectures I already implemented
in previous videos to create an autoencoder. You’ll also learn to
train an autoencoder with the MNIST dataset.
This video is part of a series called “Generating Sound with
Neural Networks”. In this series, you’ll learn how to generate
sound from audio files and spectrograms 🎧 🎧 using Variational
Autoencoders 🤖 🤖
Here’s the video:
https://www.youtube.com/watch?v=6fZdJKm-fSk&list=PL-wATfeyAMNpEyENTc-tVH5tfLGKtSWPp&index=6
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Hey guys, so I also made this stackoverflow post if you want to
see it:
https://stackoverflow.com/questions/65662127/what-is-the-equivalent-of-get-collection-for-the-frozen-inference-model
So when testing my predictions are correct on the model,
normally I write get_collection(“logits”).
However, for a .pb/inference model I can’t do this anymore
because the saver.restore only works for checkpoints, not .pb, so
even though I can get tensors, I’m not sure how to get this.
What is the equivalent action for the inference model?
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Electric vehicle upstarts have gained a foothold in the industry and are using NVIDIA DRIVE to keep that momentum going. Nowhere is the trend of electric vehicles more apparent than in China, the world’s largest automotive market, where electric vehicle startups have exploded in popularity. NIO, Li Auto and Xpeng are bolstering the initial growth Read article >
The post New Year, New Energy: Leading EV Makers Kick Off 2021 with NVIDIA DRIVE appeared first on The Official NVIDIA Blog.
I’m reading through some academic code and I’ve come across a
line that looks like this
gen1 = tf.concat(3, [gen1, e6])
According to the Tensorflow
documentation, the signature is
tf.concat( values, axis, name='concat' )
which means the line I found doesn’t make any sense. I thought
that perhaps it was different in an older version, but it’s the
same in the
Tensorflow 1 documentation too!
When I switch the arguments, it works. Is there any explanation
for this madness?
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Hello everyone, I am starting to use tensorflow and I would like
to know if you can recommend an updated tutorial on how to create
my own object detector using the tensorflow api (learning
transfer), thanks !!
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