imsmile2000 / Hand-bone-Semantic-Segmentation-Project

level2_cv_semanticsegmentation-cv-11 created by GitHub Classroom

Hand Bone Image Segmentation

This project is the Naver Boost Camp CV11 team's submission code for Hand Bone Image Segmentation competition.
Given an image of hand bone, it is a matter of segmenting 29 bone parts.

Team Members

김용희	박승희	이윤표	이준하	주재영

Environment

• OS : Linux Ubuntu 18.04.5
• GPU : Tesla V100 (32GB)

Folder Structure

├─eda
├─ensemble
├─mmdetection
├─mmdetection3
├─UniverseNet
├─yolov8
├─multilabel_kfold.py
└─streamlit

Usage

Install Requirements

• pip install -r requirements.txt

train.sh

1. Move the path to the tools folder where the train.sh file is located

2. Write python3 train.py command in train.sh file

   python3 train.py --model FCN --loss bce_loss --epochs 100

   All arguments for config training

    python3 train.py [-h] [--seed SEED] [--loss LOSS] [--model MODEL] [--epochs EPOCHS] [--val_every VAL_EVERY] [--train_batch TRAIN_BATCH] [--train_workers TRAIN_WORKERS] [--wandb WANDB] [--encoder ENCODER]
                [--save_dir SAVE_DIR] [--model_path MODEL_PATH] [--debug DEBUG] [--transform TRANSFORM] [--acc_steps ACC_STEPS] [--dataclean DATACLEAN]

3. Run

   nohup sh train.sh

Result

Loss experiment result

Metric : Dice coefficient

model	encoder	loss	wandb	public
MAnet	resnet101	bce	0.9516	0.9456
MAnet	resnet101	smp_dice	0.9513	0.9489
MAnet	resnet101	calc	0.9523	0.9484
MAnet	resnet101	smp_focal	0.9506	0.9452
MAnet	resnet101	bce_with_logit	0.9492
MAnet	resnet101	jaccard	0.9538	0.9496
MAnet	resnet101	tversky	0.9524	0.9490
MAnet	resnet101	comb(bce: 0.5, dice: 0.5)	0.9526	0.9500
MAnet	resnet101	comb1(bce: 0.33, dice:0.33, jaccard: 0.33)	0.9524	0.9490
MAnet	resnet101	comb2(bce: 1, dice: 3, jaccard: 6)	0.9528	0.9500
MAnet	resnet101	comb3(bce: 0.1, dice:0.6, jaccard: 0.3)	0.9531	0.9503

We use comb3 as our loss. You can use it by set argument when training "--loss comb_loss"

Augmentation experiment result

Metric : Dice coefficient

augmentation	wandb	리더보드
ElasticTransform(300)	0.9527	0.9507
Rotate(limit=45)	0.9524	0.9502
RandomContrast(limit=[0,0.5], p=1)	0.9617	0.9497 (inference transform: 0.9500)
RandomContrast (limit=0.2, p=0.5)	0.9515	0.9484
RandomContrast(limit=[0,0.5], p=0.5)	0.9493	0.9471
Normalize	0.9486	0.9465
ElasticTransform(400)	0.9524	0.9450
Crop	0.9479
CenterCrop	0.9343	0.9243 (inference transform: 0.2927)
Equalize and remove black (200)	0.6426(100) →0.7891 (200)

We use ElasticTransform(300), Rotate(limit=45) and RandomContrast(limit=[0,0.5], p=1) as our final augmentation.

TTA experiment result

Metric : Dice coefficient

TTA	리더보드
TTA 적용 안했을 때	0.9710
HorizontalFlip	0.9710
HorizontalFlip, Multiply([0.9,1,1.1,1.2])	0.9717
HorizontalFlip, Multiply([0.9,1,1.1,1.2]), Rotate 90	0.9585

We use HorizontalFlip, Multiply([0.9,1,1.1,1.2]) as our final TTA combination

Final Solution

Metric : Dice coefficient

• Final submission : Public : 0.9743(2nd) / Private : 0.9749(2nd)