aue8088-pa2's Introduction

HYU-AUE8088, Understanding and Utilizing Deep Learning

PA #2. Object Detection

Important Files

├── README.md
├── requirements.txt
├── datasets
│   └── nuscenes/ (see below explanation)
├── data
│   ├── ...
│   └── nuscenes.yaml
├── models
│   ├── ...
│   ├── yolo.py
│   └── yolo5n_nuscenes.yaml
├── utils
│   ├── ...
│   ├── dataloaders.py
│   └── loss.py
├── detect.py
├── debug.ipynb
└── train_simple.py

Preparation

Prepare dataset (4.3GB, resized images with bbox labels, front camera only)

$ wget https://hyu-aue8088.s3.ap-northeast-2.amazonaws.com/nuscenes_det2d.tar.gz
$ tar xzvf nuscenes_det2d.tar.gz

Create python virtual environment

$ python3 -m venv venv/aue8088-pa2
$ source venv/aue8088-pa2/bin/activate

Check whether the virtual environment set properly : The result should end with venv/aue8088-pa2/bin/python.
```
$ which python
```
Clone base code repository (replace ircvlab to your account if you forked the repository)
```
$ git clone https://github.com/ircvlab/aue8088-pa2
```

[!] Create a symbolic link for nuscenes dataset

Assume the below folder structure

├── nuscenes_det2d
├── aue8088-pa2
│   ├── data/
│   ├── models/
│   ├── train_simple.py
│   ├── ...
│   └── README.md (this file)

Follow below commands

$ cd aue8088-pa2
$ mkdir datasets
$ ln -s $(realpath ../nuscenes_det2d) datasets/nuscenes
$

Install required packages
```
$ pip install -r requirements.txt
```

Train

Command

$ python train_simple.py \
  --img 416 \
  --batch-size 64 \
  --epochs 40 \
  --data data/nuscenes.yaml \
  --cfg models/yolov5n_nuscenes.yaml \
  --weights yolov5n.pt \
  --workers 16 \
  --name yolov5n

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