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Building a Poor Man’s Deep Learning Camera in Python

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Re: Building a Poor Man’s Deep Learning Camera in Python

#6
This is the project I am planning to do with my son as he's getting interested in computers and loves nature. The rough aims are -

> Rasp Pi + Camera/PIR to photograph birds > Connect to internet and post to wp, twitter and instagram

The final aim is to add an AI component to see if we can detect birds and keep a count

Re: Building a Poor Man’s Deep Learning Camera in Python

#8

This is the project I am planning to do with my son as he's getting interested in computers and loves nature. The rough aims are - > Rasp Pi + Camera/PIR to photograph birds > Connect to internet and post to wp, twitter and instagram The final aim is to add an AI component to see if we can detect birds and keep a count

Do you have hummingbirds in your area? You could setup a high fps camera pointed at hummingbird feeder. Hummingbirds aren't afraid to approach a feeder placed just outside the window of a house.

Re: Building a Poor Man’s Deep Learning Camera in Python

#9
post #7

This is an awesome family xmas project. I don't have an old pc around to run YOLO / YOLO Tiny constantly though; can anyone recommend a cheap, suitable server provider for this? AWS EC2?

Don't you have a computer with a decent GPU? I have trained YOLOv2 on a GTX 1050. A night of training (and starting from pre-trained lower layers) yields good results depending on your application.

Re: Building a Poor Man’s Deep Learning Camera in Python

#10

how does this compare with https://aiyprojects.withgoogle.com/vision ? which one is the cheapest and the most funnest to work with ?

(EDIT due to wrongly stating that models run on the Raspberry Pi directly)

The Google Vision Kit will run models on a custom neural processing chip connected to the Raspberry Pi Zero. With the DIY setup from the blog bost, the neural network runs on a "large pc" (potentially with GPU). Depending on the hardware you have at your disposal, you can run more complex (and therefore more powerful) neural networks. At the same time, you'll need wifi set-up and streaming to work. Completely embedded devices are easier to just put in the wild.

In theory, you should be able to use the models from the Vision Kit if you follow their instructions and just put the on a Raspberry Pi directly, and get an additional Movidius compute stick: https://developer.movidius.com/

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