UDACITY CAPSTONE PROJECT

PROJECT OVERVIEW The intent of this project is to leverage the power of Convolutional Neural Networks to train a model that could identify if a given input image is "Dog" or "Human" and the dog's breed (or the closest breed it resembles with in case of human face as the input).

Instructions

1. Clone the repository and navigate to the downloaded folder.

git clone https://github.com/prvnk10/udacity_nanodegree_capstone
cd udacity_nanodegree_capstone

2. Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/udacity_nanodegree_capstone/dogImages.

3. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/udacity_nanodegree_capstone/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.

4. Donwload the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/udacity_nanodegree_capstone/bottleneck_features.

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. Open the notebook.

jupyter notebook dog_app.ipynb

Libraries used

• keras
• numpy
• glob
• sklearn
• cv2
• matplotlib
• tqdm
• PIL

Blog post link: https://prvnk10.medium.com/udacity-nanodegree-capstone-dog-breed-classification-a83d6fd43286