Self-Driving Cars Project for NYCU Courses, Fall 2023
Used existing radar images to train a detector with the goal of recognizing various vehicles in the images (e.g., trucks, minibuses, bicycles, buses)
Verified model performance using data from Guangfu Road, Hsinchu, Taiwan
├── bonus_data
│ ├── images
│ │ ├── train
│ │ └── val
│ ├── labels
│ │ ├── train
│ │ └── val
│ └── train_data # train_data from google drive bonus folder
│ └── ...
├── runs
│ └── dectect # weights will be saved here
├── ultralytics # tools for yolo
│ └── ...
├── yolo_best # best weights and json for competition and bonus
│ ├── yolov8l_bonus_train6_best_pred.json
│ ├── yolov8l_bonus.pt
│ ├── yolov8m_.json
│ └── yolov8m_.pt
├── yolov8_dataset
│ ├── images
│ │ ├── train
│ │ └── val
│ └── labels
│ ├── train
│ └── val
├── all_eva #Evaluation code
├── data_process_bonus.ipynb # generate labels for traning bonus model
├── data_process.ipynb # generate labels for traning competition model
├── dataset.yaml
├── README.md
├── weights_to_json.py
├── yolov8l.pt
├── yolov8m.pt
├── yolov8n.pt
├── yolov8s.pt
└── yolov8x.pt
The best model performance was achieved using YOLOv8m.
| parameters | default value | optimizer value |
|---|---|---|
| epochs | 100 | 65 |
| optimizer | auto | Adam |
| learning rate(lr0) | 0.01 | 0.0001 |
| patience | 50 | 25 |
| augmentation | True | True |
| batch size | 16 | 16 |
video : https://youtu.be/BmGU1UOnfzY
This project is released under the MIT License.