Machine-Learning-Experiment

Project: Dog Breed Recognition

Deep Learning

Summary

Main

• dog_app.ipynb (html)

Content

• Detect humans face using OpenCV

• Detect dogs using ResNet-50

• Classify dog breads
  • CNN
    • Test accuracy: 2.1531%
  • CNN using transfer learning (VGG-16)
    • Test accuracy: 47.6077%
  • CNN using transfer learning (VGG-19)
    • Test accuracy: 85.2488%
• Build app
  • Dog

    

  • Human

    

  • Others

    

Project Description

Install

This project requires Python and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn
• TensorFlow
• Keras
• OpenCV
• tqdm

You will also need to have software installed to run and execute a Jupyter Notebook

Data

1. Clone the repository and navigate to the downloaded folder.
2. Download the dog dataset. Unzip the folder and place it at location dogImages/.
3. Download the human dataset. Unzip the folder and place it at location lfw/.
4. Download the VGG-16 bottleneck features for the dog dataset. Place it at location bottleneck_features/.

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run the following commands. This will open the Jupyter Notebook and project file in your browser.

jupyter notebook dog_app.ipynb