In this project, a dog breed classifier is implemented based on the template provided by Alexis Cook on https://github.com/udacity/dog-project. The user can supply an image of a dog and the neural network predicts the breed. Alternatively, the user can supply an image of a human, then the code will identify the resembling dog breed.
The following instructions are taken from https://github.com/udacity/dog-project. They show how you will get you a copy of the project up and running on your local machine for development and testing purposes.
- Clone the repository and navigate to the downloaded folder.
git clone https://github.com/gro1m/dog-project.git
cd dog-project
-
Download the dog dataset. Unzip the folder and place it in the repo, at location
path/to/dog-project/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
torequirements/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
torequirements/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
torequirements/dog-windows-gpu.yml
):
conda env create -f requirements/dog-windows.yml activate dog-project
- Linux (to install with GPU support, change
-
(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
torequirements/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
torequirements/requirements-gpu.txt
):
conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt
- Linux or Mac (to install with GPU support, change
-
(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"
- Linux or Mac:
-
(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).
To test your own image, add a cell under Step 7: Test Your Algorithm and add Xception_detector(image_path)
and replace image_path
with the path to your image.
This project is licensed under the MIT License - see LICENSE.txt file for details
The major contribution to this project is given by Alexis Cook with the template available from https://github.com/udacity/dog-project.