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https://github.com/ruhyadi/vehicle-detection-yolov8
Vehicle Detection with YOLOv8
https://github.com/ruhyadi/vehicle-detection-yolov8
Last synced: about 2 months ago
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Vehicle Detection with YOLOv8
- Host: GitHub
- URL: https://github.com/ruhyadi/vehicle-detection-yolov8
- Owner: ruhyadi
- Created: 2023-09-02T15:25:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-03T14:47:01.000Z (over 1 year ago)
- Last Synced: 2024-08-02T01:23:16.794Z (5 months ago)
- Language: Python
- Size: 1.12 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Vehicle Detection with YOLOv8
## 🏁 Introduction
YOLOv8 is a real-time object detection model developed by [Ultralytics](https://github.com/ultralytics/ultralytics). This repository demonstrate how to train YOLOv8 on [KITTI](https://www.kaggle.com/datasets/didiruh/capstone-kitti-training) dataset and use it to detect vehicles in images and videos. Then we will deploy the trained model as an API server using [FastAPI](https://fastapi.tiangolo.com/).
## 👀 Quickstart
Quickstart will guide you to run vehicle detection API server using Docker. If you want to train YOLOv8 on KITTI dataset, please refer to the [Installation](#installation) section.
We assume that you have [Docker](https://www.docker.com/) installed. First, we need to download the trained model from [Release](https://github.com/ruhyadi/vehicle-detection-yolov8/releases/tag/v0.0) page. You can download the model by running the following command:
```bash
cd tmp
wget https://github.com/ruhyadi/vehicle-detection-yolov8/releases/download/v0.0/vehicle_kitti_v0_last.pt
cd ..
```
You should have the model in the `tmp` folder.
Next, we need to build the Docker image. To build the Docker image, run the following command:
```bash
docker build -f dockerfile.api -t ruhyadi/vehicle-detection-api:latest .
```
You should have the Docker image with the name `ruhyadi/vehicle-detection-api:latest`.
Now, we can run the Docker container. To run the Docker container, run the following command:
```bash
bash scripts/start_api.sh
# output
vehicle-yolov8-api | Available providers: ['CPUExecutionProvider']
vehicle-yolov8-api | Setup YOLOv8 ONNX engine...
vehicle-yolov8-api | Setup YOLOv8 ONNX engine... Done
vehicle-yolov8-api | Starting uvicorn server on 0.0.0.0:7000...
vehicle-yolov8-api | INFO: Started server process [1]
vehicle-yolov8-api | INFO: Waiting for application startup.
vehicle-yolov8-api | INFO: Application startup complete.
vehicle-yolov8-api | INFO: Uvicorn running on http://0.0.0.0:7000 (Press CTRL+C to quit)
```
You can access the API server Swagger at `http://localhost:7000`.
## 📥 Installation
### 📦 Create a virtual environment
We assume that you have [Anaconda](https://www.anaconda.com/) installed. To install the required packages, run the following commands:
```bash
conda create -n yolov8 python=3.10 cudatoolkit=11.8
conda activate yolov8
```
### 🐍 Install PyTorch
Next, install PyTorch with the following command:
```bash
# CUDA 11.8
pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 --index-url https://download.pytorch.org/whl/cu118
# CPU only
pip install torch==2.0.0+cpu torchvision==0.15.1+cpu --index-url https://download.pytorch.org/whl/cpu
```
### 🚓 Download KITTI dataset
Download the [KITTI](https://www.kaggle.com/datasets/didiruh/capstone-kitti-training) dataset and extract it to the `data` folder.
You should have the following folder structure:
```bash
data
├── kitti
│ ├── image_2 # images from the left color camera
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ ├── ...
│ │ └── 007480.png
│ ├── label_2 # label files in KITTI format
│ │ ├── 000000.txt
│ │ ├── 000001.txt
│ │ ├── ...
│ │ └── 007480.txt
│ ├── calib # calibration files
│ │ ├── 000000.txt
| │ ├── 000001.txt
│ │ ├── ...
│ │ └── 007480.txt
```
In order to train YOLOv8 with KITTI dataset, the first step we need to rename `image_2` to `images`. You can do this by running the following command:
```bash
mv data/kitti/image_2 data/kitti/images
```
You should have the following folder structure:
```bash
data
├── kitti
│ ├── images # images from the left color camera
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ ├── ...
│ │ └── 007480.png
│ ├── label_2 # label files in KITTI format
│ │ ├── 000000.txt
│ │ ├── 000001.txt
│ │ ├── ...
│ │ └── 007480.txt
│ ├── calib # calibration files
│ │ ├── 000000.txt
| │ ├── 000001.txt
│ │ ├── ...
│ │ └── 007480.txt
```
### 🔧 Convert KITTI format to YOLO format
KITTI dataset uses a different format than YOLO. To convert the KITTI format to YOLO format, run the following command:
```bash
python tools/kitti2yolo.py \
--images_dir data/kitti/images \
--labels_dir data/kitti/label_2 \
--output_dir data/kitti
```
You should have the following folder structure:
```bash
data
├── kitti
│ ├── images
| ├── label_2
│ ├── calib
│ └── labels # label files in YOLO format
│ ├── 000000.txt
│ ├── 000001.txt
│ ├── ...
│ └── 007480.txt
```
### 🪛 Create YOLO Training and Validation sets
To create the YOLO training and validation sets, run the following command:
```bash
python scripts/generate_yolo_sets.py \
--images_dir data/kitti/images \
--output_dir data/kitti \
--train_val_split 0.80 \
--prefix yolo
```
You should have the following folder structure:
```bash
data
├── kitti
│ ├── images
| ├── label_2
│ ├── calib
│ ├── labels
│ ├── yolo_train.txt # YOLO training set
│ └── yolo_val.txt # YOLO validation set
```
### 🖨️ Create a YAML Configuration File
Create a YAML configuration file for training. You can use the `dataset/yolov8s.yml` file as a template. The configuration file should be placed in the `configs` folder.
## 🥋 Training
All sets are ready. Now, we can start training. To train YOLOv8, run the following command:
```bash
python src/train.py \
--weights yolov8s.pt \
--config configs/dataset/kitti.yaml \
--epochs 15 \
--batch-size 4 \
--img-size 640 \
--device 0 \
--workers 4
```
If you want to resume training from a checkpoint, add the `--resume` flag:
```bash
python src/train.py \
--weights yolov8s.pt \
--config configs/dataset/kitti.yaml \
--epochs 15 \
--batch-size 4 \
--img-size 640 \
--device 0 \
--workers 4 \
--resume
```
You can also use the `--weights` flag to specify a custom weight file.
## 🔋 Export to ONNX
ONNX (Open Neural Network Exchange) is an open format for representing deep learning models. To export the trained model to ONNX, run the following command:
```bash
python tools/torch2onnx.py \
--weights_path tmp/vehicle_kitti_v0_last.pt
```
You should have exported ONNX model in the `tmp` folder.
## 💌 Akwnowledgement
- [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics): YOLOv8 is a real-time object detection model developed by Ultralytics.
- [FastAPI](https://fastapi.tiangolo.com/): FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.