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if cars are facing in a different direction when coming and going that should be enough for automl to classify based on this feature. You can also trigger on absence of a particular car (missing object search). There is really almost no limit on what can be done with automl..

@ilovecoffee Thank you for the code sample. worked great on the first try! awesome stuff So far I'm only detecting USPS truck, but will add more goodness.. now that the workflow is known. I'm letting my camera detect motion and upload a picture by FTP to the NAS (synology). A script...

it looks like google's own automl vision image classification training is based on this example https://colab.research.google.com/github/tensorflow/examples/blob/master/community/en/flowers_tf_lite.ipynb they are using MobileNet V2 as the base, which explains how they are able to build a new...

Good deal thanks I'm trying to figure out how to load the .pb file exported from google into tensorflow and continue training it. I got google to train for two labels (w/ 1 node-hour for free) and their results are pretty good. I wanted to add another label or add more images and that's when...

@ilovecoffee I was able to create a google model and export the .tflite file Do you have an example in python of how to classify an image using this exported model? I have the python env setup and tensorflow locally. The google docs go deep fast. Also if I wonder if it's possible to...

I've used your idea to ID vehicles via sighthound. If it finds a USPS truck in the image my script sends a message+photo to a private telegram channel, notifying me of the USPS delivery. All other motion detection is sent to a second "muted" telegram channel (don't want to be bothered for...

once the model is computed running predictions should not require a lot of cpu cycles. Most cell phones are able to do that easily without draining the battery, for example scene or face detection in live view. I'm thinking about using a rasberry pi or beaglebone to process images.