Capsule endoscopy detection DACON challenge

Related tags

Deep Learningcapsule_endoscopy_detection
Overview

capsule_endoscopy_detection (DACON Challenge)

Overview

  • Yolov5, Yolor, mmdetection기반의 모델을 사용 (총 11개 모델 앙상블)
    • 모든 모델은 학습 시 Pretrained Weight을 yolov5, yolor, mmdetection 및 swin transformer github로부터 받아서 사용
    • 각 방식에 필요한 형태로 데이터의 format 변경
  • Train set과 Validation set을 나누어 진행
  • 총 11개의 결과를 앙상블
    • detectors_casacde_rcnn_resnet50_multiscale, retinanet_swin-l, retinanet_swin-l_multiscale, retinanet_swin-t, atss_swin-l_multiscale, faster_rcnn-swin-l_multiscale, yolor_tta_multiscale, yolov5x, yolov5x_tta, yolov5x_tta_multiscale
    • Weighted boxes fusion (WBF) 방식으로 앙상블 진행 (Iou threshold = 0.4)
    • 모델에 관한 보다 자세한 내용은 /all_steps 폴더 내에 STEP2로 시작하는 .sh 스크립트들에 적힌 주석을 참고해주세요!

환경(env) 세팅

  • 실험 환경: Ubuntu 18.04, Cuda 11.3, Anaconda3, Python 3.8
  1. git clone ( + 폴더 권한 설정)
git clone https://github.com/MAILAB-Yonsei/capsule_endoscopy_detection.git
chmod -R 777 capsule_endoscopy_detection
cd capsule_endoscopy_detection
  1. cbnet만 제외한 나머지에 대한 env 생성 (all_except_cbnet)
conda create -n all_except_cbnet python=3.8
conda activate all_except_cbnet
pytorch 설치 (ex. conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch)
pip install openmim
mim install mmdet
pip install -r requirements_all_except_cbnet.txt
conda deactivate
  1. cbnet에 대한 env 생성 (cbnet)
conda create -n cbnet python=3.8
conda activate cbnet
pytorch 설치 (ex. conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch)
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
     (ex. pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10.0/index.html)
cd UniverseNet
pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"
pip install instaboostfast
pip install git+https://github.com/cocodataset/panopticapi.git
pip install git+https://github.com/lvis-dataset/lvis-api.git
pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
pip install pandas
pip install tqdm
pip install shapely
conda deactivate
cd ..

main code 실행

[각 STEP 별로 자세한 설명은 /all_steps 폴더 내의 각각의 .sh 파일에 적힌 주석을 참고해주세요!]

STEP0. data root path 지정

cd all_steps
gedit data_path.txt

data_path.txt 파일에 data의 절대 경로를 명시한다!!! (ex. /mnt/data)

STEP1. data preparation (약 20~30분 소요)

conda activate all_except_cbnet
bash STEP1_data_preparation.sh

STEP2. 각 모델을 학습시킨다. (pretrained 모델로 inference만 하고자 한다면 바로 STEP3로!)

  • cbnet만 제외한 나머지에 대한 Training
conda activate all_except_cbnet
bash STEP2_train_model1_atss_swin-l_ms.sh
bash STEP2_train_model2_detectors_cascade_rcnn_r50_ms.sh
bash STEP2_train_model3_faster_rcnn_swin-l_ms.sh
bash STEP2_train_model4_retinanet_swin-l.sh
bash STEP2_train_model5_retinanet_swin-l_ms.sh
bash STEP2_train_model6_retinanet_swin-t_ms.sh
bash STEP2_train_model7_yolor.sh
bash STEP2_train_model8_yolo5x.sh
  • cbnet에 대한 Training
conda activate cbnet
bash STEP2_train_model9_cbnet_faster_rcnn_swin-l_ms.sh

STEP3. 모든 모델에 대해 Inference를 진행한다. (모델 하나당 20~30분 소요)

  • STEP2.를 건너뛰고 pretrained 모델에 대해 test를 하는 경우 아래 과정을 수행한 뒤 STEP3.의 명령어를 실행:
    • 아래의 weight 파일 링크에서 받은 mmdetection/ckpts 폴더를 /mmdetection 폴더 아래에 위치시킨다.
    • 아래의 weight 파일 링크에서 받은 UniverseNet/ckpts 폴더를 /UniverseNet 폴더 아래에 위치시킨다.
    • 아래의 weight 파일 링크에서 받은 YOLO/ckpts 폴더를 /YOLO 폴더 아래에 위치시킨다.
    • weight 파일 링크: https://drive.google.com/drive/folders/151KJC3FTUsK5mfx4TtNbhiFuuvLIeGz-?usp=sharing
  • cbnet만 제외한 나머지에 대한 Inference
conda activate all_except_cbnet
bash STEP3_inference_all_except_cbnet.sh
  • cbnet에 대한 Inference
conda activate cbnet
bash STEP3_inference_cbnet.sh

SETP4. 모든 모델에 대해 앙상블을 진행한다.

conda activate all_except_cbnet
bash STEP4_ensemble.sh
  • 최종 파일은 가장 상위 디렉토리에 'final.csv'로 생성!!!

주의사항

모두 순서에 맞게 코드를 구성해놓았기 때문에 하나의 코드를 2번 실행하는 등의 경우 진행에 어려움이 있을 수 있습니다. 참고해주세요.

현재 코드는 validation은 진행하지 않게 주석처리했습니다. 원하시면 predict.py에서 validation 주석처리를 풀고 val_answer.csv 파일의 경로를 설정해주시면 됩니다.

(predict.py 파일 위치: /mmdetection/predict/main.py, /UniverseNet/predict/main.py)

Owner
MAILAB
Medical Artificial Intelligence Laboratory at Yonsei University, Republic of Korea
MAILAB
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