RANZCR-CLiP 7th Place Solution

This repository is WIP. (18 Mar 2021)

Installation

git clone https://github.com/analokmaus/kaggle-ranzcr-clip-public.git 
cd kaggle-ranzcr-clip-public
git clone https://github.com/analokmaus/kuma_utils.git 

kuma_utils is a toolbox I use for competitions and work. Check it out!

conda

conda env create -n {NEW NAME} -f environment.yaml

docker

WIP

IMPORTANT: timm version
Since segmentation_models_pytorch requires timm=0.3.2 which does not include ResNet200D.
I added latest timm=0.3.4 as timm_latest in the root directory.
In case you need ResNet200D, you must use import timm_latest.

Download datasets

┣ data
┃   ┣ ranzcr-clip
┃       ┣ (competition files)
┃       ┣ nih_chestxray
┃       ┃   ┣ (nih dataset)
┃       ┣ padchest
┃       ┃   ┣ (padchest dataset)
┃       ┣ mimic
┃           ┣ (mimic dataset)
┃
┣ kaggle-ranzcr-clip-public
    ┣ scripts

competition files

kaggle competitions download ranzcr-clip-catheter-line-classification

nih dataset

kaggle datasets download nih-chest-xrays/data

padchest dataset

kaggle datasets download raddar/padchest-tubes

mimic dataset

Due to the license, we cannot host MIMIC CXR dataset.
Please go to MIMIC CXR official website and download by yourself.

Benchmark

UNet-CNN (R1)

CV: 0.9661
Public LB: 0.970
Private LB: 0.973

python train.py --config Segmentation13
python train.py --config SegAndCls12
python inference.py --config SegAndCls12 # generate pseudo labels
python train_external.py --config PretrainStudent08l
python train.py --config SegAndCls12external6

UNet-CNN (E1)

CV: 0.9660
Public LB: 0.972
Private LB: 0.973

python train.py --config Segmentation15
python train.py --config SegAndCls14
python inference.py --config SegAndCls14 # generate pseudo labels
python train_external.py --config PretrainStudent09
python train.py --config SegAndCls14external2

Vanilla CNN (N1)

CV: 0.9671
Public LB: 0.970
Private LB: 0.972

(run training script by Y.Nakama)
python inference.py --config SingleModel02 # generate pseudo labels
python train_external.py --config Distillation03
python train.py --config SingleModel02external0

Test Environment

Adjust batch_size and relevant parameters (learning rate etc.) when you run script.

A machine with four V100 16GB (64GB total) was used to train the following configs:

• Segmentation13
• Segmentation15
• SegAndCls12*
• SegAndCls14*
• PretrainStudent08*
• PretrainStudent09*

A machine with two GF RTX 3090 24GB (48GB total) was used to train the following configs:

• SingleModel02*
• Distillation03