[planb]

I'd love to see some sample code. I've tried something similar to detect the postman in my security camera stream, but failed over lots of small technical details.

Does anyone know a good tutorial on reinforcement learning that describes a practical application like this?

[hmcdona1]

Yeah, the author of this article likely just followed these TensorFlow tutorials. You don't even have to touch code.

Follow this to retrain the Inception network: https://www.tensorflow.org/tutorials/image_retraining

Follow this to run an image through your model for classification: https://www.tensorflow.org/tutorials/image_recognition

[theikkila]

I wanted to try out different types of final layers (SVM, Logistic Reg.) so the direct example didn't suit very well for that kind of testing out. For starters that good though.

[theikkila]

Hello author of the article here!

I will probably publish in some time (when I'm not so busy doing other projects) some code samples too but until that I can give you tips.

You should start inspecting how the pre trained models (Inception v3/v4) works, what kind of layers they have and then decide what layers you want to use and what not. In case of Tensorflow, the tensorboard is very good tool for inspecting the model inner layers.

If you want to get started even more easier you should probably take a look into Tensorflow Slim models (https://github.com/tensorflow/models/tree/master/slim)

There is quite beginner friendly instructions for simple fine tuning of the models and it should take you pretty far.

[apendleton]

The first lesson in the Fast.ai deep learning course covers exactly this (retraining a pre-trained network, in this case VGG, to distinguish between cats and dogs). There's a video and an accompanying ipython notebook with sample code, using Keras.

http://course.fast.ai/lessons/lesson1.html