🛰️SW중심대학 공동 AI 경진대회 2023 (Satellite Image Building Area Segmentation)

[PIXEL Team]
ColdTbrew
hyjk826
uijinee
junghyun2moon

Index

• 대회 사이트
• 주제 선정 배경
• InternImage 🌃
  • Install mmseg
  • Pretrained Checkpoints
  • How to start training
  • How to start inference
• SWINv2 🌌
  • Install mmseg
  • Pretrained path
  • Pretrained Checkpoints
  • How to start training
  • How to start inference
• Mask2Former 🖼️
  • Install mmseg
  • Pretrained Checkpoints
  • How to start training
  • How to start inference
• Ensemble 🎯
  • Ensemble Script
  • Extra Ensemble
  • System Environment

대회 사이트

데이콘

주제 선정 배경

InternImage 🌃

Install mmseg

Installation

Pretrained Checkpoints

1. INTERN_best_mDice_iter_336000.pth
   Download Link

2. INTERN_k1_best_mDice_iter_210000.pth
   Download Link

3. INTERN_k3_best_mDice_iter_220000.pth
   Download Link

4. INTERN_k4_best_mDice_iter_280000.pth
   Download Link

How to start training

   cd mmseg_0.x.x
   python segmentation/train.py work_dirs/no4/INTERN_config.py

How to start inference

   cd mmseg_0.x.x
   python segmentation/inference.py

SWINv2 🌌

Install mmseg

Installation

Pretrained path

1. Swin Pretrained pth
   Download Link

Pretrained Checkpoints

1. swin_best_mDice_iter_320000.pth (160k + 160k)
   Google Drive Link

How to start training

1. Training:

   cd mmseg_1.x.x
   python tools/train.py work_dirs/swin/swin_config.py
   

How to start inference

cd mmseg_1.x.x
python work_dirs/swin/infer.py

Mask2Former 🖼️

Install mmseg

Installation

Pretrained Checkpoints

1. m2f_K2_best_mDice_iter_90000.pth (90k)
   Google Drive Link

2. m2f_K3_best_mDice_iter_90000.pth (90k + 90k)
   Google Drive Link

3. m2f_K4_best_mDice_iter_90000.pth (90k + 90k)
   Google Drive Link

How to start training

1. K2 Training:

   cd mmseg_1.x.x
   python tools/train.py work_dirs/mask2former/m2f_config_k2.py
   

2. K3 Training:

   cd mmseg_1.x.x
   python tools/train.py work_dirs/mask2former/m2f_config_k3.py
   

3. K4 Training:

   cd mmseg_1.x.x
   python tools/train.py work_dirs/mask2former/m2f_config_k4.py
   

How to start inference

cd mmseg_1.x.x
python work_dirs/mask2former/infer_m2f.py

Ensemble 🎯

Ensemble

1. Swin (단일 모델)
2. internimage
   • best_mDice_iter_336000 + k1 + k3 + k4 (threshold = 2)
3. mask2former
   • k2 + k3 + k4 (threshold = 2)

last submit
swin + internimage + mask2former (threshold = 2)

Extra ensemble

Ensemble_by_weight
점수를 최대한 높이기 위해 csv 앙상블의 단점인 threshold를 다양하게 적용해 보기 위해 여러 제출 .csv 파일을 이용해 각각 submit.csv의 public score를 기준으로 각각 가중치를 주어 앙상블 후 ensemble8_21_th0.35.csv 생성해 최고 public score를 0.8226를 도달함

System environment: 🖥️

• sys.platform: linux
• Python: 3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0]
• CUDA available: True
• numpy_random_seed: 1545188287
• GPU 0: A100-SXM4-40GB
• CUDA_HOME: /usr/local/cuda
• NVCC: Cuda compilation tools, release 11.0, V11.0.221
• GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
• PyTorch: 1.12.1

PyTorch compiling details:

• GCC 9.3

• C++ Version: 201402

• Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications

• Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)

• OpenMP 201511 (a.k.a. OpenMP 4.5)

• LAPACK is enabled (usually provided by MKL)

• NNPACK is enabled

• CPU capability usage: AVX2

• CUDA Runtime 11.3

• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37

• CuDNN 8.3.2 (built against CUDA 11.5)

• Magma 2.5.2

• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS=-fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

• TorchVision: 0.13.1

• OpenCV: 4.8.0

• MMEngine: 0.8.2

Runtime environment:

• cudnn_benchmark: True
• mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
• dist_cfg: {'backend': 'nccl'}
• seed: 1545188287
• Distributed launcher: none
• Distributed training: False
• GPU number: 1