Dog Breed Classifier

Dog breed classifier built with Convolutional Neural Networks (CNN). Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Dataset

The dataset contains 133 total dog categories and has 8351 total dog images. The dataset is provided by Udacity and it can be downloaded from this link. We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.

Final Model

After experimenting with several CNN models, the final model is created with Transfer Learning. The base model is ResNet-50 and it is trained 20 epochs. It achieved 80.02% test accuracy.

Getting Started

Clone the repository and navigate to the downloaded folder.

git clone https://github.com/TetsumichiUmada/dog_app.git
cd dog_app

Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog_app/dogImages.
Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.
Donwload the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features.
(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.
(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
```
(Optional) If you are running the project on your local machine (and not using AWS) and Step 6 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
```
(Optional) If you are using AWS, install Tensorflow.

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

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"

(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"

Open the notebook.

jupyter notebook dog_app.ipynb

(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.

Acknowledgments

Udacity Deep Learning Nanodegree Program