HYU-AUE8088, Understanding and Utilizing Deep Learning

Project. Multispectral Pedestrian Detection

Important Files

bash ├── README.md ├── requirements.txt ├── datasets │   └── kaist-rgbt/ (see below explanation) ├── data │   ├── ... │   └── kaist-rgbt.yaml ├── models │   ├── ... │   ├── common.py │   ├── yolo.py │   ├── yolo5n_kaist-rgbt.yaml │   └── yolo5s_kaist-rgbt.yaml ├── utils │   ├── ... │   ├── eval │ │ ├── coco.py │ │ ├── cocoeval.py │ │ └── kaisteval.py │   ├── dataloaders.py │   └── loss.py ├── detect.py ├── debug_kaist.ipynb ├── val.py └── train_simple.py

Preparation

• Prepare dataset (5.8GB, multispectral(visible + lwir) images with bbox labels) bash $ wget https://hyu-aue8088.s3.ap-northeast-2.amazonaws.com/kaist-rgbt-aue8088.tar.gz $ tar xzvf kaist-rgbt-aue8088.tar.gz

• Create python virtual environment bash $ python3 -m venv venv/aue8088-project $ source venv/aue8088-project/bin/activate

• Check whether the virtual environment set properly : The result should end with venv/aue8088-project/bin/python.

bash $ which python

• Clone base code repository (replace ircvlab to your account if you forked the repository) bash $ git clone -b project https://github.com/ircvlab/aue8088-pa2

If you already forked the above repository, then you can checkout to project branch. bash $ git fetch origin $ git checkout -b project origin/project

• [!] Create a symbolic link for kaist-rgbt dataset

  • Assume the below folder structure

  bash ├── kaist-rgbt ├── aue8088-pa2 │   ├── data/ │   ├── models/ │   ├── train_simple.py │   ├── ... │   └── README.md (this file) - Follow below commands bash $ cd aue8088-pa2 $ mkdir datasets $ ln -s $(realpath ../kaist-rgbt) datasets/kaist-rgbt $

• Install required packages bash $ pip install -r requirements.txt

Train

• Command bash $ python train_simple.py \ --img 640 \ --batch-size 16 \ --epochs 20 \ --data data/kaist-rgbt.yaml \ --cfg models/yolov5n_kaist-rgbt.yaml \ --weights yolov5n.pt \ --workers 16 \ --name yolov5n-rgbt \ --rgbt \ --single-cls

Evaluation (eval.ai server)

• On your labtop, go to the website: http://166.104.168.170:8888/
  • Only available in Hanyang internal network
  • If you're not in campus, please use VPN (https://vpn.hanyang.ac.kr)
    • It takes a day (or two) to get the permission from IT department.
• Sign up
• Send a message to me via LMS (then, I'll manually verify your account.)
• Go to All Challenges - Multispectral Pedestrian Detection Challenge - Submit
• Upload your predictions on test-all-20.txt
  • If you run train_simple.py with the default setting, predictions on test-all-20.txt will be generated: runs/train/*/epoch*_predictions.json
  • You can download this file onto your computer.
  • Note: if size of the prediction file is too large (about > 30MB), evaluation on the server could be failed.