From f44a73ca84142c6e8ddd5a271daa208fd0bdeecb Mon Sep 17 00:00:00 2001 From: jjjuuuun Date: Mon, 22 May 2023 15:23:43 +0900 Subject: [PATCH] Docs: Add README.md --- README.md | 147 ++++++++++++++++++ ...5\217\254\355\212\270(03\354\241\260).pdf" | Bin 0 -> 1536686 bytes imgs/private.png | Bin 0 -> 13133 bytes imgs/project_image.png | Bin 0 -> 41139 bytes imgs/public.png | Bin 0 -> 13362 bytes 5 files changed, 147 insertions(+) create mode 100644 README.md create mode 100644 "docs/ObjectDetection_CV_\355\214\200\353\246\254\355\217\254\355\212\270(03\354\241\260).pdf" create mode 100644 imgs/private.png create mode 100644 imgs/project_image.png create mode 100644 imgs/public.png diff --git a/README.md b/README.md new file mode 100644 index 0000000..6c730d0 --- /dev/null +++ b/README.md @@ -0,0 +1,147 @@ +# ♻️ 재활용 품목 분류를 위한 Object Detection + +## ✨ 팀 소개 + +Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): + + + + + + + + + +

김승기
+
💻 + 🔣 + 🚇 + 🚧 +

김준영

+ 💻 + 🚇 + 🚧 + 📆 +

전형우

+ 💻 + 🤔 + 👀 + ⚠️ +

천지은

+ 💻 + 🔣 + 💡 + 🔬 +

신우진

+ 💻 + 🤔 + 🚇 + 👀 +
+ +This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! + +## 🎙️ 프로젝트 소개 + +

+ +

+ +요즘 쓰레기 관련 문제들이 항상 문제로 떠오르고 있습니다. 이러한 문제를 해결할 수 있는 방법 중 하나는 분리수거를 잘 하는 것입니다. 잘 분리 배출 된 쓰레기는 자원으로서 가치를 인정받아 재활용되지만, 잘못 분리 배출 되면 그대로 폐기물로 분류되어 매립 또는 소각 되기 때문입니다. 따라서 분리 배출을 잘 하기 위해서 이미지에서 쓰레기를 detection하는 모델을 만들어 보려고 합니다. 쓰레기를 잘 detection하는 모델을 만든다면 쓰레기장에 설치되어 정확한 분리수거를 도울 수 있고 어린 아이들의 분리수거 교육에 사용될 수도 있을 것입니다. + +이번 프로젝트는 `부스트캠프 AI Tech` CV 트랙내에서 진행된 대회이며 mAP50으로 최종평가를 진행하게 됩니다. + +## 📆 프로젝트 일정 + +프로젝트 전체 일정 + +- 2023.05.01 ~ 2023.05.18 + +프로젝트 세부 일정 + +- 2023.05.01 ~ 2023.05.05 : Object Detection에 대해 알아보기 +- 2023.05.06 ~ 2023.05.08 : EDA +- 2023.05.09 ~ 2023.05.10 : Training Dataset과 Validation Dataset으로 분리, MMDetection 사용법 익히기 +- 2023.05.11 ~ 2023.05.12 : Online Augmentation +- 2023.05.13 ~ 2023.05.14 : Offline Augmentation, Model 실험 +- 2023.05.15 ~ 2023.05.17 : TTA(Test Time Augmentation), Multi-scale Training & Testing +- 2023.05.18 ~ 2023.05.18 : Ensemble + +## 🥼 프로젝트 수행 + +➡️ Link따라 들어가시면 보실 수 있습니다. + +1. [EDA](https://calico-dance-4bf.notion.site/EDA-c883e3e6f9654e81805047fbc831a1dc) +2. [Augmentation](https://calico-dance-4bf.notion.site/Augmentation-f31b33a09cdb42c3941863028fadcda2) +3. [Model-MMDetection](https://calico-dance-4bf.notion.site/Model-215ceba896e54e74ada78787fdc8666a) +4. [Model-YOLOv8](https://calico-dance-4bf.notion.site/YOLOv8-05e8ebf98d8541d58e1b465266209003) +5. [Wandb 실험 결과](https://wandb.ai/ganisokay/recycle_trash/reports/Copy-of-kimseungki1011-s-Wrap-up-report-Graphs--Vmlldzo0NDE5Nzk5) + +## 🗒️ 프로젝트 결과 + +- TTA, Multi-scale Training & Testing, Ensemble을 통해 최종적으로 아래와 같은 결과를 얻었습니다. + - Public + + - Private + + +# 🔄️ Directory + +```bash +├── docs +├── imgs +├── upsampling +├── yolov8 +├── .gitingnore +├── gitcommit_template.txt +├── README.md +└── mmdetection + ├── configs + │ └── _teamconfig_ + │ ├── datasets + │ ├── models + │ ├── schedules + │ ├── utils + │ ├── example + │ │ ├── base_config.py + │ │ ├── coco_detection.py + │ │ ├── default_runtime.py + │ │ ├── faster_rcnn_r50_fpn.py + │ │ └── schedule_1x.py + │ ├── [tag]ExpName_V1 + │ └── [tag]ExpName_V2 + ├── tools + │ ├── train.py + │ └── test.py + └── run_experiments.sh +``` + +- 하나의 실험은 `example` 폴더처럼 구성되어 있습니다. +- 실험의 폴더 이름은 어떤 실험을 하는지 알 수 있도록 `[tag]ExpName_V1`처럼 구성했습니다. + + | Tag | Description | + | ----------- | ----------------------------------------------------------------------------- | + | aug | Online augmentation & Offline augmentation 실험 | + | optim | Optimizer 실험 | + | model | MMDetection에서 지원하는 model 실험(2-Stage) | + | pycocotools | Pycocotools에서 지원하는 small_bbox, medium_bbox, large_bbox의 크기 조절 실험 | + +- MMDetection에서 지원하지 않는 YOLOv8 같은 경우 root directory에 있는 `yolov8` 폴더를 따로 구성하여 프로젝트를 진행했습니다. +- 실험의 결과를 확인하기 위한 기능들, 성능을 더 올리기 위한 기능들은 `utils`폴더 안에 모두 구성했습니다. + |File(.ipynb)|Description| + |---|---| + |check|Test dataset에 대해 bbox가 어떻게 그려지는지 확인| + |checklist|Validation dataset에 대해 모델 검증| + |ensemble|Ensemble을 한 번에 할 수 있도록 하나의 파일로 구성| + |plot_cm|Model이 class를 어떻게 예측하고 있는지 알 수 있는 Confusion Matrix| + |pseudo_labeling|Pseudo Labeling을 할 수 있도록 함| + |show_bbox|Validation dataset에 대해 bbox가 어떻게 그려지는지 확인| +- Offline Augmentation을 할 수 있는 코드는 root directory에 있는 `upsampling` 폴더에서 보실 수 있습니다. + +# 🤔 Wrap-Up Report + +- [Wrap-Up Report]() + +# ⚠️ Dataset 출처 + +- `mmdetection/configs/_teamconfig_/utils`에 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