This article on a user who used a TX2 with RealSense may be of interest to you.
There is currently no information available on the processor compatibility of the new D-cameras, so I cannot recommend any boards that would match up to those camera models yet, unfortunately.
Agree about Intel not recommending anything for D4 yet. However, since Intel has recommended ZR300+Joule in past, I believe Intel would suggest using D4 on Joule to start with. I believe that Intel will either release a successor of Joule with higher processing power, or provide a Joule+ASIC solution. But as you say, we can only guess. Only Intel can tell with certainty.
At the time of the cancelation of Joule, Edison and Galileo, some users speculated that a newer model of Up Board called the Core may provide all the features in one board that the canceled boards provided separately.
I didn't know about Up board. Now I checked about it and see that it is much more powerful - 12 cores. I wonder if it's a practical solution for mobile application. It may be consuming too much power and generating too much heat?
As the Computer Vision & AI is strongly shifting towards Neural Networks, soon the generic processors will be out of choice. Atom or ARM is noway suitable for Neural computations. Graphics processor like Jetson will do a much better job. Intel may be already thinking about this & discontinuing Joule may be a step in that direction? I am curious to know about performance of the Movidius Compute Stick. If it does a decent job, one could just combine this with one of the Ubuntu powered boards to do all robotics stuff. The generic processor only needs to run the OS, and all heavy duty computation can be handled by Movidius stick. Use multiple sticks if one is not sufficient. So you really don't need Joule, a less powerful solution may do the job.
The reason I didn't order Movidius compute stick myself was that it only supports Caffe. I wish it could support TensorFlow and other models. I was in no mood to learn Caffe just to try their compute stick. But now I am giving it a second thought.
Regarding Joule, Intel is still selling those dev kits, and it will continue shipping till Dec 2017. Funny thing is that their ZR300 product page officially recommends using Joule dev kit.
So unless they plan to discontinue ZR300 too by year end, they should put an alternative to Joule dev kit in place :-)
I believe that if one is aware that a product is going to be withdrawn from sale in the near future then it is still a valid choice so long as (a) you are aware its software is not going to receive further updates, (b) the current state of its software meets your immediate project needs, and (c) the price of the hardware is attractive as a result of impending cancelation (like with the current RealSense kit sale in the Intel Click online store til Sept 30 or whilst stocks last).
As the D-cameras support indoor and outdoor use, it would make them a natural successor to the ZR300. As the ZR300 developer kit was only launched in March 2017, I'd expect it to be on sale for another year yet before retirement, but I have no official insight on that matter.
Intel's strategy has increasingly been to develop "reference designs" for new products and then invite manufacturers to create their own version of that design (e.g the SR300-compatible Razer Stargazer and Creative BlasterX Senz3D cameras). This approach is also planned be used for Intel's forthcoming 'Project Alloy' Realsense-based "merged reality" headset, assuming that it is still in development and on course for the previously announced Q4 2017 release window.
So it is conceivable that rather than manufacture a successor to the Joule, Intel may choose to put its support behind another manufacturer's product, much like how the original Up Board was bundled with the R200 camera in the RealSense Robotic Development Kit (also on heavily discounted sale right now in the Click store, plug plug!)
Yes, neural systems with humble-powered hardware can do a lot now. I was reading yesterday about how a new neural learning program for analyzing galactic astronomy images can generate results in seconds even running on a smartphone, whereas before it could take a month to calculate the results.
Wading through the myriad of options is pretty rough.
Its why I was hoping Intel would create a design standard that could carry this forward.
As I am trying to guess a path forward, the DCamera is coming out this month, YAY.
If it does some of the things I hope I will be in line to get one. Information is still sparse.
I just watched a TED talk on YOLO and they seem to have come very far on human recognition
(over 90% I think he mentioned) This is great news for me but what CPU demand this had to do
in realtime was unknown. Here is hoping that there will be an Intel low power VPU alternative that can handle this.
The Up boards look very interesting.
You've probably seen this already, but Intel employee Bolous AbuJaber posted a tutorial for implementing YOLO using Euclid's cameras and TensorFlow. Perhaps the principles in that tutorial may be adaptable for the D-cameras, though the script code may have to be rewritten for SDK 2.0 if such an adaptation of the principles were possible for the D-camera hardware..
"and it achieves around 1FPS on Euclid."
I stopped reading after this. 1 fps isn't fast enough.
about 4/5 of the way through he is doing multiple object recognition in near
real-time on a phone. Euclid cant go as fast a phone???
l will PM Bolous AbuJaber to check if that 1 FPS figure is correct.
Hi Marty, Thanks for sharing. As ChicagoBob pointed out 1 fps is not something practical. However, one option I see is that one could run this powerful algorithm at 1fps or even less frequently to get good scene understanding. At the same time one could run a less powerful algorithm more frequently to post process the scene. Whenever I get D4 kits in hand, these things will be good to try out. Keep sharing the wonderful resources.
Yes, I imagine that 1 frame per second would be like a click - click - click press of the button on an ordinary manual photography camera. You potentially miss a lot of detail inbetween the button presses that would make it useless for real-time image gathering like a moving creature in a nature photo, but could be useful under certain circumstances where you only need to take a periodic snapshot of what the camera is observing.
It's going to be great to see what people do with the technology such as longer range, higher frame rate and real-time sensing that the new D-cameras provide.
I have several comments about this but most pressing that I hope that someone could give some feedback.
How a cell phone can get near realtime YOLO performance while full blown cpus can not.
What is secret to making this happen? The GPU? The ARM? The memory speed?
CNN's should be a snap for SSE3 asm optimization since it has many matrix multiplication
operations so a phone beating an Atom?
Maybe with someone familiar with the source code for the phone side could comment on how
they pulled this off. I am sure Intel has connections with the YOLO group
so maybe they can help. Intel for sure has people that can read the phone source code.
The hardware suggested at the Darknet website is completely out of date as
they are running this on a cell phone and a laptop (and no one has said what kind of laptop it is)
I think there are 2 parts to your question.
(1) How some of the cellphones are achieving Very high accuracy on Object Recognition, whereas CPUs can't. The high accuracy on Object Recognition has been possible using Deep Learning & in particular CNN. Now to do the computations for a Neural Network, a generic CPU is not a optimum hardware. Neural Networks are math heavy, in particular lot of matrix multiplications. The closest analogy I can give is that computations are similar to those involve in Computer Graphics. Just like you need a powerful graphics processor to get good gaming performance, you need a special Processor or ASIC to shine on Neural Computations. Google's TPU or Qualcomm's Hexagon DSP are Deep Learning friendly processors. Since Nvidia already had Graphics Processors, and Neural Networks require similar computations, Nvidia also has some good offerings. There are also some cloud based offerings, where you could offload the heavy processing. Intel's Movidius is a similar powerful chip well suited for deep learning. Some of the cellphones which I know to be providing good object recognition performance use Qualcomm's Hexagon DSP. If not for this specialize hardware, performance will be nowhere close. As MartyG mentioned in one of the posts, Intel has now announced Myriad X VPU. This VPU should give us state of the art performance, but it is not clear when this will be available. So a long wait here I suppose.
(2) Second issue which I see is perceived performance. The performance as depicted in promotional videos or in specific research papers is some time not very realistic. It works well only on limited test environment, but not in real world. Apart from the data set & environment, 2 other important factors which can significantly impact the required processing power are Accuracy & no of categories.
I don't know about you but the wild west of AI seems to be going in 10 directions at the same time.
With resource constrained R&D budgets we all have to be really choosy as to what road to chase.
I ordered a Movidius Compute Stick and while I waited found out it was only able to do single object detection
and did not have a tensor flow acceleration yet. So I ended up canceling the order.
The Movidius X chip seems exciting but with no release date I can't spend cycles on vaporware.
I put a lot of eggs into the Tensorflow basket since it was open source and had the most github downloads.
I have experimented with Keros which is more generic and of course slower.
I am slowly moving toward YOLO due to the impressive numbers.
Driven by the industry changes our goal at my company does not include thousands of
generic classifications but high inference accuracy of approximately 100 to 200 classifications.
Waiting for the D435 to be released and all it brings to the game of change and hoping a
release data for the Movidius X chip will be released soon as I have to pick and stick to a direction
by end of September.
Wonder if Intel will be bringing all this to CES like NVidia does? I will be there this year and would love
to get the story one on one.