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mmseg_WS


📰 Contributors

CV-16조 💡 비전길잡이 💡
NAVER Connect Foundation boostcamp AI Tech 4th

민기 박민지 유영준 장지훈 최동혁

📰 Links

  • 비전 길잡이 Notion 📝( not finished )

📰 Objective

  • weighted_sum head에 대한 성능 검증

📰 Dataset

ADE20K
Purpose Fine-tuning
Num_classes 150
Training set 20,210 images
Validation set 2,000 images
|-- ADEChallengeData2016
    |-- image
    |   |-- train
    |   `-- val
    `-- mask
        |-- train
        `-- val

WS Decode Head

  • Stage Features Upsample

  • Weighted Sum 적용


📰 Result

⚙️ Installation

git clone https://github.com/revanZX/mmseg_WS.git

🧰 How to Use

FineTuing Example

# with WS
python tools/train.py \
       ./configs_custom/pvt_v2/fpnws_pvt_v2_b5_ade20k_160k.py \
       --work-dir ../work_dir/pvtws_v2_b5 \
       --seed 1

# with NON WS
python tools/train.py \
       ./configs_custom/pvt_v2/fpn_pvt_v2_b5_ade20k_160k.py \
       --work-dir ../work_dir/pvt_v2_b5 \
       --seed 1

📰 Directory Structure

|-- 🗂 appendix          : 발표자료 및 WrapUpReport
|-- 🗂 segformer         : HuggingFace 기반 segformer 모델 코드
|-- 🗂 boostformer       : Segformer 경량화 모델 코드
|-- 🗂 imagenet_pretrain : Tiny-ImageNet encoder 학습시 사용한 코드
|-- 🗂 util              : tools 코드 모음
|-- Dockerfile
|-- train.py             : ADE20K Finetuning 코드
|-- eval.py              : 모델 Inference 결과 출력 코드
|-- requirements.txt
`-- README.md

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