A pipeline of neural networks to process real-world, user-supplied images. If given an image of a dog, the algorithm will identify an estimate of the canine’s breed else if supplied an image of a human, the code will identify the resembling dog breed.
Open and view the Project using the .zip
file provided or at my Github Repository
The project has been hosted on Github
- Overview
- Getting Started
- Running the App
- Development
- Stopping the App
- Evaluation
- Submission
- References
Welcome to the Convolutional Neural Networks (CNN) project in the Deep Learning Foundations Nanodegree! In this project, I learned how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. 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.
Along with exploring state-of-the-art CNN models for classification, I made important design decisions about the user experience for your app. The goal is that by completing this lab, I understood the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. My imperfect solution will nonetheless create a fun user experience!
You would require the following tools to develop and run the project:
The starter project can be downloaded from here
-
Clone the repository and navigate to the downloaded folder.
git clone https://github.com/udacity/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
.
Start by installing python and anaconda
-
(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"
-
-
(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"
-
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.
NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included.
To run the project:
- Activate the conda environment as mentioned in the 6th and 7th point of the Installation section
- Start the Jupyter Notebook as mentioned in the 11th point of the Installation section
- Press the
play
▶️ icon to start the execution of cells. The output will be visible right below the cells.
Follow the instructions in the notebook; they will lead you through the project. You'll be editing the dog_app.ipynb
file.
Once you're done with the app, stop it gracefully using the following command:
- Select
File -> Close and Halt
inside jupyter notebook - Press
Ctrl+c
in the cli - Deactivate and Delete (if finished with the project) the environment
>> conda deactivate dog-project # Deactivate the environment >> conda remove --name dog-project --all # Delete the environment
The project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass.
When you are ready to submit your project, collect the following files and compress them into a single archive for upload:
- The
dog_app.ipynb
file with fully functional code, all code cells executed and displaying output, and all questions answered. - An HTML or PDF export of the project notebook with the name
report.html
orreport.pdf
. - Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the
dogImages/
orlfw/
folders. Likewise, please do not include thebottleneck_features/
folder.
Alternatively, your submission could consist of the GitHub link to your repository.
- Keras Docs on Convolutional
- Loss Functions on Andrej Karpathy's tumblr
- Keras Cheat Sheet
- Batch Normalization