https://github.com/cyyeh/dog-breed-classifier-project

Udacity Machine Learning Engineer Nanodegree Capstone Project
https://github.com/cyyeh/dog-breed-classifier-project

computer-vision deep-learning fastapi gcp-cloud-run mobilenetv2 python3 pytorch webapp

Last synced: about 2 months ago
JSON representation

Udacity Machine Learning Engineer Nanodegree Capstone Project

• Host: GitHub
• URL: https://github.com/cyyeh/dog-breed-classifier-project
• Owner: cyyeh
• License: mit
• Created: 2020-06-07T04:02:38.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2023-01-06T08:41:34.000Z (about 2 years ago)
• Last Synced: 2024-10-27T21:37:07.454Z (3 months ago)
• Topics: computer-vision, deep-learning, fastapi, gcp-cloud-run, mobilenetv2, python3, pytorch, webapp
• Language: JavaScript
• Homepage: https://dog-breed-classifier.netlify.app/
• Size: 59.4 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 24
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Dog Breed Classification Project

![Visualization of the codebase](./diagram.svg)

## Project Functionalities

- Users can know more about dog breeds and their information through taking photos or uploading photos.

- Users can also directly use photos provided by the project to quickly play around.

- Users can get top 3 dog breed prediction results with wikipedia links.

- Users can learn English and Chinese at the same time.

## System Architecture

![](system-diagram.png)

- dog detection

  - model: MobileNet V2 pretrained model without finetuning

  - input: an image having RGB channels with Base64 encoding

  - output: boolean

- dog classification

  - model: MobileNet V2 pretrained model with finetuning

  - input: an image having RGB channels with Base64 encoding

  - output: top-3 prediction results(dog breed names and prediction probabilities)

## Project Structure

- api: backend

- web: frontend

## Used Tools/Technologies

- frontend

  - HTML/CSS/JavaScript

  - Materialize

  - Babel

  - Webpack

- backend

  - Python

    - FastAPI

  - Docker

  - GCP

    - Cloud Run

- deep learning

  - PyTorch

## Deep Learning Under the Hood in This Project

You can check [this repo](https://github.com/cyyeh/ml-engineering-ndegree/tree/master/projects/capstone-project) for further details, and this project is orginally from my Udacity Machine Learning Engineer Nanodegree capstone project. However, I have changed some model details for this dog breed classifier, you can check [here](https://github.com/cyyeh/ml-engineering-ndegree/tree/master/projects/capstone-project/deployment) for more details.

## Setup

### Frontend

> please see the web folder

- `npm install` (download dependencies first)

- local development

  - `npm run start`

- deployment

  - `npm run build`

You can checkout other commands in `package.json` in the web folder

### Backend

> please see the api folder

- local development without docker:

  - `pip install pipenv` (If you don't have `pipenv` installed)

  - `pipenv install`

  - `pipenv run uvicorn main:app --reload`

Below commands are already written in `Makefile`, please checkout and change any setting you need first.

- local development using docker:

  - `make dev`

- build a docker image:

  - `make build`

- submit a docker image to Google Container Registry:

  - `make submit`

- deploy a docker image in Google Container Registry to Google Cloud Run:

  - `make deploy`

## Todos

- [x] support English

- [ ] support PWA

  - [ ] Android

  - [ ] iOS

- [ ] export the trained PyTorch models to edge devices for better inference performance