Install conda, then:
conda create -n cil python=3.8
conda activate cil
pip install -r requirements.txt
pre-commit installFrom the root of this repository:
kaggle competitions download -c cil-road-segmentation-2022
unzip cil-road-segmentation-2022.zip
mkdir data
mv training data
mv test dataThen with python:
from glob import sample
import os
VAL_SIZE = 10
for img in sample(glob("data/training/images/*.png"), VAL_SIZE):
os.rename(img, img.replace('training', 'validation'))
mask = img.replace('images', 'groundtruth')
os.rename(mask, mask.replace('training', 'validation'))The code/run.py script runs with the following arguments, and needs to be executed from root directoy of this repo:
python code/run.py [-h] [--model-type {small,base}]
[--loss {bce,dice,mixed,focal,twersky,f1,patch-f1}]
--train-dir TRAIN_DIR [--model-save-dir MODEL_SAVE_DIR] [--no-augment]
--val-dir VAL_DIR --test-dir TEST_DIR [--n_epochs N_EPOCHS]
[--batch_size BATCH_SIZE] [--checkpoint_path CHECKPOINT_PATH]
{baseline-svc,baseline-unet,baseline-patch-cnn,unet,swin-unet}| Argument | Description | Choices | Default value |
|---|---|---|---|
model (positional) |
Model to use for training | baseline-svc, baseline-unet, baseline-patch-cnn, unet, swin-unet |
- |
--train-dir |
Path to the training directory | - | None, required |
--val-dir |
Path to the validation directory | - | None, required |
--test-dir |
Path to the test directory | - | None, required |
--model-save-dir |
Path where the model will be saved | - | . |
--no-augment |
If added to the command, the dataset will not be augmented (i.e. the initial dataset will be used instead) | - | . |
--n_epochs |
Number of epochs to train on | - | 100 |
--batch_size |
Path of a model checkpoint to load to resume training | - | 128 |
--model-type |
For model swin-unet (will be ignored otherwise), to select which pre-trained model we would like to use |
small, base |
base |
--checkpoint_path |
Path of a model checkpoint to load to resume training | - | None |
-l, --loss |
Loss to train with | bce, dice, mixed, focal, twersky, f1, patch-f1 |
bce |
From the root of this folder:
Support Vector Classifier baseline (baseline-svc)
python code/run.py baseline-svc \
--train-dir "data/training" \
--test-dir "data/test" \
--val-dir "data/validation" \Patch-CNN baseline (baseline-patch-cnn)
python code/run.py baseline-patch-cnn \
--train-dir "data/training" \
--test-dir "data/test" \
--val-dir "data/validation" \
--n_epochs 20 \
--batch_size 128Vanilla-UNet baseline (baseline-unet)
SAVE_DIR="<where you want the best weights to be stored>"
python code/run.py baseline-unet \
--train-dir "data/training" \
--test-dir "data/test" \
--val-dir "data/validation" \
--n_epochs 35 \
--batch_size 4 \
--model-save-dir $SAVE_DIRFine-tuning UNet (unet)
Basic usage:
SAVE_DIR="<where you want the best weights to be stored>"
N_EPOCHS=200
BATCH_SIZE=4
LOSS="<choose your loss>"
python code/run.py unet \
--train-dir "data/training" \
--test-dir "data/test" \
--val-dir "data/validation" \
--loss $LOSS \
--n_epochs $N_EPOCHS \
--batch_size $BATCH_SIZE \
--model-save-dir $SAVE_DIRUSwinBaseNet (swin-unet)
Basic usage:
SAVE_DIR="<where you want the best weights to be stored>"
N_EPOCHS=200
BATCH_SIZE=2
LOSS="<choose your loss>"
python code/run.py swin-unet \
--train-dir "data/training" \
--test-dir "data/test" \
--val-dir "data/validation" \
--loss $LOSS \
--n_epochs $N_EPOCHS \
--batch_size $BATCH_SIZE \
--model-save-dir $SAVE_DIRYou can make create an ensemble submission with this Python script, with which you can add as many .csv files as you want.
The final result is determined by majority vote.
python tools/create_ensemble_submission.py file1.csv file2.csv ... -o "submission/ensemble.csv"