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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.
The project can achieve FCWS, LDWS, and LKAS functions solely using only visual sensors. using YOLOv5 / YOLOv5-lite / YOLOv6 / YOLOv7 / YOLOv8 / YOLOv9 / EfficientDet and Ultra-Fast-Lane-Detection-v2 .
| Date | Stars |
|---|---|
| 2026-07-24 | 276 |
| 2026-07-25 | 276 |
| 2026-07-28 | 276 |
| 2026-07-30 | 276 |
| 2026-08-06 | 276 |
Today
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growth rate 0.00%/day
# Vehicle-CV-ADAS
<p>
<a href="#"><img alt="Python" src="https://img.shields.io/badge/Python-14354C.svg?logo=python&logoColor=white"></a>
<a href="#"><img alt="OnnxRuntime" src="https://img.shields.io/badge/OnnxRuntime-FF6F00.svg?logo=onnx&logoColor=white"></a>
<a href="#"><img alt="TensorRT" src="https://img.shields.io/badge/TensorRT-49D.svg?logo=flask&logoColor=white"></a>
<a href="#"><img alt="Markdown" src="https://img.shields.io/badge/Markdown-000000.svg?logo=markdown&logoColor=white"></a>
<a href="#"><img alt="Visual Studio Code" src="https://img.shields.io/badge/Visual%20Studio%20Code-ad78f7.svg?logo=visual-studio-code&logoColor=white"></a>
<a href="#"><img alt="Windows" src="https://img.shields.io/badge/Windows-0078D6?logo=windows&logoColor=white"></a>
</p>
Example scripts for the detection of lanes using the [ultra fast lane detection v2](https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2) model in ONNX/TensorRT.
Example scripts for the detection of objects using the [YOLOv5](https://github.com/ultralytics/yolov5)/[YOLOv5-lite](https://github.com/ppogg/YOLOv5-Lite)/[YOLOv6](https://github.com/meituan/YOLOv6)/[YOLOv7](https://github.com/WongKinYiu/yolov7)/[YOLOv8](https://github.com/ultralytics/ultralytics)/[YOLOv9](https://github.com/WongKinYiu/yolov9)/[YOLOv10](https://github.com/ultralytics/ultralytics)/[EfficientDet](https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch) model in ONNX/TensorRT.
Add [ByteTrack](https://github.com/ifzhang/ByteTrack) to determine the driving direction of ID vehicles and perform trajectory tracking.
# ➤ Contents
1) [Requirements](#Requirements)
2) [Examples](#Examples)
3) [Demo](#Demo)
4) [License](#License)
<h1 id="Requirements">➤ Requirements</h1>
* **Python 3.7+**
* **OpenCV**, **Scikit-learn**, **onnxruntime**, **pycuda** and **pytorch**.
* **Install :**
The `requirements.txt` file should list all Python libraries that your notebooks
depend on, and they will be installed using:
```
pip install -r requirements.txt
```
<h1 id="Examples">➤ Examples</h1>
* ***Download YOLO Series Onnx model*** :
Use the Google Colab notebook to convert
| Model | release version | Link |
| :------------- |:-------------------------------- | :------------------------------------------------ |
| YOLOv5 | `v6.2` | [](https://colab.research.google.com/drive/1mwoA3_-f3QIcHtLSuGN5WVszKeZ_i366?usp=sharing) |
| YOLOv6/Lite | `0.4.0` | [](https://colab.research.google.com/drive/1FhyQvDUzUVgPwYB1DSADfCm_CG09D9Ab?usp=sharing) |
| YOLOv7 | `v0.1` | [](https://colab.research.google.com/drive/1arGcVT32Sm3zxhql2jgAa5xIEZdsDq9D?usp=sharing) |
| YOLOv8 | `8.1.27` | [](https://colab.research.google.com/drive/1mrhgTaZFQWWwhf0jcMwD_tOjmXfMh3pS?usp=sharing) |
| YOLOv9 | `v0.1` | [](https://colab.research.google.com/drive/12oFXgco3CARzhU8CiLCpf_6oBA3sAvPT?usp=sharing) |
| YOLOv10 | `8.2.41` | [](https://colab.research.google.com/drive/1RqkZj6u0iwZknGt_VI4J4h93gNzwF94P?usp=sharing) |
* ***Convert Onnx to TenserRT model*** :
Need to modify `onnx_model_path` and `trt_model_path` before converting.
```
python convertOnnxToTensorRT.py -i <path-of-your-onnx-model> -o <path-of-your-trt-model>
``Excerpt of 7,227 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c98225fb16372c6c, topic:tensorrt, topic:onnxruntime
matched fp:c98225fb16372c6c, topic:pytorch
matched fp:c98225fb16372c6c, topic:object-detection