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Nice! Does anyone know, is it possible to train such models on RPi (probably not) or similar sized computer? EDIT: I guess the question should be: is there a RPi-like sized machine available which is more suitable for training ANN?
Inference times for inception-v3 on a Raspberry Pi have been benchmarked recently [1]. They weigh in at around 2s. If you remove the python layer it gets down to 500ms. However, it's not yet clear if the python overhead is the only reason for that. Nvidia's Tegra X1 should supposedly be capable of And finally, Sam who also facilitated building TF on the Pi is about to host a 6 weeks half theory, half practice course…
But real life images are at least 640*480, not imagenet.
Earlier quoted context omitted.
"Just put a heatsink on the CPU." Ever had any problems without it?
The cpu throttles when it gets too hot. Won't cause damage to not have it, but will slow it down.
Either that or it was meant for outdoors operation in arctic regions.
Earlier quoted context omitted.
Hey Sam this is Matt, thanks for your comment and your help a few months back! And for anyone else reading this, Sam is great at getting back to filed issues about installing tensorflow on a Pi: https://github.com/samjabrahams/tensorflow-on-raspberry-pi/i...
Yeah, I'm using Sam's TF wheel on RPi3 and it works great. > it was not feasible to analyze every image captured image from the PiCamera using TensorFlow, due to overheating of the Raspberry Pi when 100% of the CPU was being utilized Just put a heatsink on the CPU. It's like $1.50 ... $1.95 on Adafruit. I glue a heatsink to every RPi3 unit I build. https://www.adafruit.com/products/3082 https://www.adafruit.com/produ…
Even with the heatsink (which we install on all of the Pis), we were still having overheating issues. We tried a few other things too to mitigate the problem: 1. Reducing sampling rate for the image recognition (but if we reduced this beneath several seconds we could miss the express trains) 2. Using a cooling fan (https://www.amazon.com/gp/product/B013E1OW4G/ref=oh_aui_sear...) - still didn't prevent overheating if the CPU was continuously loaded at 100%. 3. Only sampling images where we detected motion (https://svds.com/streaming-video-analysis-python/)
We decided to use the 3rd option: Leveraging our motion detection algorithm, which while sensitive to false positives, allows us to use Deep Learning image recognition to eliminate those false positives.
Happy to chat more about your experiences daemonize-ing TF applications!
Nice! Does anyone know, is it possible to train such models on RPi (probably not) or similar sized computer? EDIT: I guess the question should be: is there a RPi-like sized machine available which is more suitable for training ANN?
A bit off topic, but related: can anyone point me to a recipe for "low-latency" video with the RPi. I don't even really mean "low-latency", I had tried a couple of different setups/tools a few months ago, and the best I could do was a half or full second delay on the video.
On the Pi3, our application processes 320X240 images at 10 FPS without any problems.
Let me know if you have any questions!
Inference times for inception-v3 on a Raspberry Pi have been benchmarked recently [1]. They weigh in at around 2s. If you remove the python layer it gets down to 500ms. However, it's not yet clear if the python overhead is the only reason for that. Nvidia's Tegra X1 should supposedly be capable of And finally, Sam who also facilitated building TF on the Pi is about to host a 6 weeks half theory, half practice course…
Does this use the Pi's GPU? It's not clear and I think the answer is no?
Inference times for inception-v3 on a Raspberry Pi have been benchmarked recently [1]. They weigh in at around 2s. If you remove the python layer it gets down to 500ms. However, it's not yet clear if the python overhead is the only reason for that. Nvidia's Tegra X1 should supposedly be capable of And finally, Sam who also facilitated building TF on the Pi is about to host a 6 weeks half theory, half practice course…
I imagine a proliferation of robots, security cameras and smart open source siri/alexas
Earlier quoted context omitted.
Yeah, I'm using Sam's TF wheel on RPi3 and it works great. > it was not feasible to analyze every image captured image from the PiCamera using TensorFlow, due to overheating of the Raspberry Pi when 100% of the CPU was being utilized Just put a heatsink on the CPU. It's like $1.50 ... $1.95 on Adafruit. I glue a heatsink to every RPi3 unit I build. https://www.adafruit.com/products/3082 https://www.adafruit.com/produ…
Hi Florin, thanks for the comment! Even with the heatsink (which we install on all of the Pis), we were still having overheating issues. We tried a few other things too to mitigate the problem: 1. Reducing sampling rate for the image recognition (but if we reduced this beneath several seconds we could miss the express trains) 2. Using a cooling fan ( https://www.amazon.com/gp/product/B013E1OW4G/ref=oh_aui_sear... ) -…
Are you seeing anything happen, other than some slight throttling?
The chip cannot fry itself. It's designed to slow down so as to stay below the dangerous temperature range.
> Happy to chat more about your experiences daemonize-ing TF applications!
Eh, that was just a fancy way of saying I do what you do. Launch the program once, and let it run forever. It performs initialization (which takes a long time), then it drops into a processing loop: wait for input / read / process / do something / repeat. Pretty basic stuff really.