Part 1: Train a classifier for 100 dog breeds. Trained model
Part 2: Create a system where the user can enroll new images of existing or new dog breeds. If the user gives a new image of a known breed to be classified, the system should output the correct breed.
Part 3: Handle previously unseen breeds.
See the train_and_test.ipynb notebook for the full report, training logs, and test results.
pip install -r requirements.txt
If you're using a conda env, you may have to do ipython kernel install --user --name=<conda_env_name> to be able to choose your conda env from the Jupyter notebook.
The recommended way to reproduce training and testing is to create a Jupyter server (jupyter notebook, open train_and_test.ipynb, and run all cells.
If you prefer to run training and testing from the command line, you can ./run.sh instead, which will convert the Jupyter notebook to a .py script and run it. But you won't get any plots that way.
./run_webapp_part1.sh
./run_webapp_part2.sh