awesome-archive / kaggle-rcic-1st

1st Place Solution for Kaggle Recursion Cellular Image Classification Challenge -- https://www.kaggle.com/c/recursion-cellular-image-classification/

1st Place Solution for Kaggle Recursion Cellular Image Classification Challenge

For the description of the solution, refer to this post.

Environment

This repository contains Dockerfile that covers all required dependencies.

By default mixed precision training and inference is used (see --fp16 flag in main.py) which is fully supported in Volta and Turing architectures.

Training

python main.py --save <experiment name> <additional arguments>

will result in:

• saving checkpoint each epoch into <experiment name>.<epoch>
• saving best checkpoint (on local validation) into <experiment name>
• saving log from training into <experiment name>.log

For additional arguments and default values see python main.py --help or main.py file.

Prediction

python main.py --mode predict --load <path to checkpoint> <additional arguments>

will result in:

• saving raw predictions into <path to checkpoint>.output or into <path to checkpoint>.output<pred suffix> if --pred-suffix is specified
• saving log into <path to checkpoint>.output.log

Raw predictions are pickled logits for each class and each test and validation image. To convert it into CSV submission or ensemble multiple raw predictions, run:

./make_submission.py -o <csv output file> <path to one or multiple raw predictions>

This script will also ensemble and print score on validation set if it's not empty (see --cv-number flag for main.py).

Examples

These examples assume data (in the same format as in Kaggle with extracted files from archives) to be in ../data.

python main.py -e 130 --pl-epoch 90 --lr cosine,1.5e-4,90,6e-5,150,0 --pl-size-func 0.6*x+0.4 --cv-number -1 --seed 0 --save /results/dn161_0
python main.py --mode predict --cv-number -1 --tta 8 --load /results/dn161_0.129
./make_submission.csv /results/dn161_0.129.output -o /results/submission_0.csv

results in submission with 0.99658 private score and 0.98826 public score.

python main.py -e 130 --pl-epoch 90 --lr cosine,1.5e-4,90,6e-5,150,0 --pl-size-func 0.6*x+0.4 --cv-number -1 --seed 1 --save results/dn161_1
python main.py --mode predict --cv-number -1 --tta 8 --load /results/dn161_1.129
./make_submission.csv /results/dn161_1.129.output -o /results/submission_1.csv

results in submission with 0.99623 private score and 0.98871 public score.

Ensembling both of them (./make_submission.csv /results/dn161_0.129.output /results/dn161_1.129.output -o /results/submission.csv) results in 0.99784 private score and 0.99187 public score.