This is the code for 'Image Classifier in TensorFlow in 5 Min on YouTube. Use this CodeLab by Google as a guide. Also this tutorial is quite helpful.

##Requirements

• docker

##Usage

You just need to make a "classifier" directory with a directory "data" inside it with all your images For example

 [any_path]/my_own_classifier/ [any_path]/my_own_classifier/data [any_path]/my_own_classifier/data/car [any_path]/my_own_classifier/data/moto [any_path]/my_own_classifier/data/bus 

and then put your image on it. This "classifier" directory will have your samples but also trained classifier after execution of "train.sh".

##Train process

Just type

 ./train.sh [any_path]/my_own_classifier 

And it will do anything for you !

##Guess process

Just type for a single guess

 ./guess.sh [any_path]/my_own_classifier /yourfile.jpg 

To guess an entire directory

./guessDir.sh [any_path]/classifier [any_path]/srcDir [any_path]/destDir 

# ./guess.sh /synced/tensor-lib/moto-classifier/ /synced/imagesToTest/moto21.jpg moto (score = 0.88331) car (score = 0.11669) 

Use an absolute file path for classifier and images because the script dos not support relative path (volume mounting)

#The Challenge

Make your own classifier for scientists, then post a clone of this repo with your retrained model in it. (you can name it retrained_graph.pb and it will be around 80 MB. If it's too big for GitHub, upload it to DropBox and post the link to it in your README)

#Credits

Credit goes to Xblaster for the majority of this code. I've merely created a wrapper.