https://github.com/gazeux33/carplatedetection
Create a pipeline of two finetunned YOLOv8 models to detect and analyze car plates
https://github.com/gazeux33/carplatedetection
carplate computer-vision deep-learning fine-tuning pipeline pytorch ultralytics yolov8
Last synced: over 1 year ago
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Create a pipeline of two finetunned YOLOv8 models to detect and analyze car plates
- Host: GitHub
- URL: https://github.com/gazeux33/carplatedetection
- Owner: Gazeux33
- Created: 2024-10-15T18:15:54.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-10-15T19:18:18.000Z (over 1 year ago)
- Last Synced: 2025-02-09T14:19:38.106Z (over 1 year ago)
- Topics: carplate, computer-vision, deep-learning, fine-tuning, pipeline, pytorch, ultralytics, yolov8
- Language: Jupyter Notebook
- Homepage:
- Size: 21.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Car Plate Detection and Recognition
The aim of this project is to create a **pipeline** with two models to detect and recognize car plates. The first model
is able to detect the car plate in an image and the second model is able to recognize the characters in the car plate.
Theses models are **finetunned Yolov8**.
## PipeLine
## Data
To train theses two models I used two differents dataset.
Dataset for the Location Model : [Car Plate Detection - YoloV8](https://www.kaggle.com/datasets/nimapourmoradi/car-plate-detection-yolov8)
Dataset for the Digits Model : [Persian Plates Digits Detection - YoloV8](https://www.kaggle.com/code/nimapourmoradi/persian-plates-digits-detection-yolov8)
## How to use
The configuration file is **config.yaml**
Install all dependensies
```bash
pip install -r requirements.txt
```
To process an image you can use this command
```bash
python main.py path_to_your_image
```