Dog-Breed-Classifier

CNN Project [Udacity Deep Learning Nanodegree]

Project Overview

Project Description

Build a convolutional neural network(CNN) with Keras to classify dog breeds and then turn the code into a web app using Flask. Given an image of a dog, the model will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed and overlay filters with dog's ears, nose and tongue.

Project Procedure

• Import datasets
  • Dog dataset
  • Human dataset
• Preprocess the data with shape (batch, rows, columns, channels)
• Write detectors
  • face detector
  • dog detector
• Create a CNN
• Compile the model
  • train, test
• Write a algorithm for the dog breeds classifier
• Turn the code into a web app using Flask

Project Results

The model can successfully detect dogs and faces and make predictions on the given image. If detect a dog, the model will identify an estimate of the dog's breed. If detect any face, the code will identify the resembling dog breed for each face and overlay filters with dog's ears, nose and tongue on it.

Getting Started

Installing

1. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step.

2. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment.

   • Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml):

   conda env create -f requirements/dog-linux.yml
   source activate dog-project
   

   • Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml):

   conda env create -f requirements/dog-mac.yml
   source activate dog-project
   

   NOTE: Some Mac users may need to install a different version of OpenCV

   conda install --channel https://conda.anaconda.org/menpo opencv3
   

   • Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml):

   conda env create -f requirements/dog-windows.yml
   activate dog-project
   

3. (Optional) If you are running the project on your local machine (and not using AWS) and Step 2 throws errors, try this alternative step to create your environment.

   • Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt):

   conda create --name dog-project python=3.5
   source activate dog-project
   pip install -r requirements/requirements.txt
   

   NOTE: Some Mac users may need to install a different version of OpenCV

   conda install --channel https://conda.anaconda.org/menpo opencv3
   

   • Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt):

   conda create --name dog-project python=3.5
   activate dog-project
   pip install -r requirements/requirements.txt
   

4. (Optional) If you are using AWS, install Tensorflow.

sudo python3 -m pip install -r requirements/requirements-gpu.txt

5. Switch Keras backend to TensorFlow.

   • Linux or Mac:

      KERAS_BACKEND=tensorflow python -c "from keras import backend"
     

   • Windows:

      set KERAS_BACKEND=tensorflow
      python -c "from keras import backend"
     

6. (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment.

python -m ipykernel install --user --name dog-project --display-name "dog-project"

7. Open the notebook.

jupyter notebook dog_app.ipynb

8. (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook.

Run Web app

1. Install Flask

pip install Flask

2. Run the app.py in command line

cd dog_breeds_web_app
python ./app.py

Data

1. Download the dog datasetDownload the dog dataset. Unzip the folder and place it in the repo.

2. Download the human dataset. Unzip the folder and place it in the repo. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.

3. Donwload the VGG-16 bottleneck features for the dog dataset. Place it in the repo.