• 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.

• 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)

• api: backend
• web: frontend

• frontend
  • HTML/CSS/JavaScript
  • Materialize
  • Babel
  • Webpack
• backend
  • Python
    • FastAPI
  • Docker
  • GCP
    • Cloud Run
• deep learning
  • PyTorch

You can check this repo 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 for more details.

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

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

• support English
• support PWA
  • Android
  • iOS
• export the trained PyTorch models to edge devices for better inference performance