| 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/products/3083 > it was taking too long to load the 85 MB model into memory, therefore I needed to load the classifier graph to memory Yeah, one of the first things you learn with TF on the RPi is to daemonize it, load everything you can initially, and then just process everything in a loop. That initialization is super-slow, but after that it's fast enough. YMMV |
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!