The problem of the hackathon consisted in classifying satellite images to check whether they had silos in them, to aid a certain start-up know where to place new silos. As a bonus task, the goal was to segment images into the classes "silo" or "not silo".
To create the environment, run conda env create -f environment.yml
.
To track training, tensorboard --logdir lightning_logs --bind_all
.
The dataset given is in ai_ready/
. The test dataset is given in test_to_send/
.
All models built are in models/
To choose which model to train, change line from models.model_unet import HackathonModel
in run_training.py
.
To launch training, python run_training.py -v version_name
.
The checkpoints for our best models are in model_weights/
.
The best model found was model_efficient
for classification. The only model made for segmentation was
model_unet
.
To run evaluation on a .csv with the same format as the ones found inside the datasets, run python run_eval -p path_to_csv
.