Doggo

Doggo is a Flask web app application to run Dog breed detection models. Based on a photo upload by a user, Doggo runs the following steps:

1. Encode uploaded photo for running dog detector, human face detector and dog breed detector models
2. If the photo is classified as a dog or a human face, it predicts dog breeds
3. For the top n predictions, Doggo calls Wikipedia and dog.ceo to find more information about the predicted breeds
4. Return results to user

Table of Contents

1. Instructions
2. File Descriptions
3. Web app
4. Licensing, Authors, and Acknowledgements

Instructions

1. Build docker image:

   docker build -t doggo .

2. The app has already trained models for detecting dog, human faces and dog breeds. In case of update, you need to save the new models at models folder
3. Running the app locally:

   docker run -p 5000:80 doggo

4. Go to http://0.0.0.0:5000

File Descriptions

• core/: All backend code for parsing images, instantiating models, running and formatting predictions, ...
• core/models/: Required mdels such as CNN Dog Breed detector and opencv human face detector model.
• templates/: Flask html files
• doggo.py: Flask app routes
• requirements.txt: Project dependencies

Web app

For an uploaded photo, the app runs the model and displays the main dog breed and probability of top n dog breeds:

When the models cannot the detect a dog or a human face in the uploaded photo, Doggo returns the following message:

Licensing, Authors, and Acknowledgements

Must give credit to Udacity for providing the dataset and guidelines to run the models. More information about that can be found at: dog-project/

Doggo is under MIT License.