youben11 / flowers-classification

Flowers Classification using Deep Learning - Udacity Pytorch Challenge

Flowers Classification

Flowers Classification using Deep Learning - Udacity Pytorch Challenge

The challenge was to build and train a Neural Network that can classify 102 species of flowers.

The training was done on this Kaggle kernel, this repo only contain an app that provide a RESTful API for users to predict flower species from pictures using that trained neural network.

How to use it?

First, you need to make sure that there is an API available before using the CLI utility (which-flower.py). At the time of writing this, the API was hosted on http://130.211.108.207:5000/pred

Then you need to setup the new IP (or hopefully a domain name) of the API if it has changed by replacing the API_URL variable

The last step is to run it:

$ ./which-flower.py globe_flower.png
[+] This flower is a globe-flower

Note

This is just an example utility

Running the server app

The server app is the one responsible of all the computations needed to guess which flower is in that image, it's running a trained neural network (a modified densenet121 network)

In order to run the server app (which is under the server directory), you have two options:

• The easy way using Docker

  You will just need to install Docker

  Then run the following command inside the server dir

  $ docker build -t flower-pred .
  $ docker run -d -p 8080:80 flower-pred
  

  The first command will build the docker image and name it 'flower-pred'

  The second command will run a container of the previously created image, this container will be listening on the port 8080, feel free to change that port to have your API listen on another port.

• The second option

  Under the server directory, run the following command

  $ pip3 install -r requirements.txt
  $ python3 app.py
  

  It's seems like this one is easier, right? A French saying tells: "les apparences sont souvent trompeuses"