eButterfly
Data exploration, training and test code for eButterfly classification project
API Quickstart
Install in a local virtualenv for development
make install
make dev
Visit http://localhost:5000
Build & run in docker container without installing locally
make build
make run
Visit http://0.0.0.0:80
TODO
Exploration phase
Expore dataset, prepare for training √ (sample set only)Run simple training √ (sample set only)Get new copy of full dataset √ (manually extracted single image observations, about 100,000 images)Get access to ebutterfly.org codebase √ (static copy, waiting on bitbucket or github)Explore iNaturalist classification API √ (works in a somewhat manual way)- Make demo page for running the model (Flask example from bumblebeewatch)
- Connect to Tensorboard or Weights & Biases control panel for keeping track of models & training.
- Use new CSV from Xinbao to fetch additional images from observations with mutiple photos
- Get more familiar with eButterfly site, where can we fit it in the UI, mockups.
- Get familiar with eButterfly code, what is the stack, where might our code live.
- Read paper from insect classification researchers.
- Research new techiniques used in the paper
- Run the code from the researchers
- Get access to Azure with eButterfly credits
- Try more advanced training - multiple models, imagenet pretraining, bbox identification and photo cropping, data augmentation, etc.
Implementation phase
- Create backend for API
- Create pipeline for training and uploading new model
- Create way to easily retrain on new samples
- Create UI for frontend, integrate API