Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
License Plate Detection using YOLOv8
| Date | Stars |
|---|---|
| 2026-07-30 | 469 |
| 2026-08-10 | 469 |
| 2026-08-18 | 471 |
| 2026-08-26 | 471 |
| 2026-09-01 | 472 |
| 2026-09-02 | 472 |
| 2026-09-03 | 473 |
| 2026-09-05 | 474 |
| 2026-09-08 | 475 |
| 2026-09-12 | 476 |
| 2026-09-14 | 477 |
| 2026-10-10 | 476 |
| 2026-10-11 | 477 |
| 2026-10-14 | 478 |
| 2026-10-16 | 479 |
| 2026-10-19 | 480 |
| 2026-10-25 | 480 |
| 2026-10-26 | 481 |
| 2026-11-04 | 482 |
Today
+1 stars today
This week
+1 stars this week
This month
+5 stars this month
Momentum
0.0
growth rate 0.21%/day
# Automatic-Number-Plate-Recognition-YOLOv8 ## Demo https://github.com/Muhammad-Zeerak-Khan/Automatic-License-Plate-Recognition-using-YOLOv8/assets/79400407/1af57131-3ada-470a-b798-95fff00254e6 ## Data The video used in the tutorial can be downloaded [here](https://drive.google.com/file/d/1JbwLyqpFCXmftaJY1oap8Sa6KfjoWJta/view?usp=sharing). ## Model A Yolov8 pre-trained model (YOLOv8n) was used to detect vehicles. A licensed plate detector was used to detect license plates. The model was trained with Yolov8 using [this dataset](https://universe.roboflow.com/roboflow-universe-projects/license-plate-recognition-rxg4e/dataset/4). - The model is available [here](https://drive.google.com/file/d/1Zmf5ynaTFhmln2z7Qvv-tgjkWQYQ9Zdw/view?usp=sharing). ## Dependencies The sort module needs to be downloaded from [this repository](https://github.com/abewley/sort). ```bash git clone https://github.com/abewley/sort ``` ## Project Setup * Make an environment with python=3.10 using the following command ``` bash conda create --prefix ./env python==3.10 -y ``` * Activate the environment ``` bash source activate ./env ``` * Install the project dependencies using the following command ```bash pip install -r requirements.txt ``` * Run main.py with the sample video file to generate the test.csv file ``` python python main.py ``` * Run the add_missing_data.py file for interpolation of values to match up for the missing frames and smooth output. ```python python add_missing_data.py ``` * Finally run the visualize.py passing in the interpolated csv files and hence obtaining a smooth output for license plate detection. ```python python visualize.py ```
Excerpt of 1,676 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:20089224b5ed22b5, topic:computer-vision, topic:opencv
matched fp:20089224b5ed22b5, topic:deep-learning