onnx-Ultra-Fast-Lane-Detection-Inference
Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.
Source: https://www.flickr.com/photos/32413914@N00/1475776461/
Pytorch inference
For performing the inference in Pytorch, check my other repository Ultrafast Lane Detection Inference Pytorch.
Requirements
- OpenCV, scipy, onnx and onnxruntime. pafy and youtube-dl are required for youtube video inference.
Installation
pip install -r requirements.txt
pip install pafy youtube-dl
ONNX model
The original model was converted to different formats (including .onnx) by PINTO0309, download the models from his repository and save it into the models folder.
ONNX Conversion script: https://github.com/cfzd/Ultra-Fast-Lane-Detection/issues/218
Original Pytorch model
The pretrained Pytorch model was taken from the original repository.
Model info (link)
- Input: RGB image of size 800 x 200 pixels.
- Output: Keypoints for a maximum of 4 lanes (left-most lane, left lane, right lane, and right-most lane).
Examples
- Image inference:
python imageLaneDetection.py
- Webcam inference:
python webcamLaneDetection.py
- Video inference:
python videoLaneDetection.py
Inference video Example
Original video: https://youtu.be/2CIxM7x-Clc (by Yunfei Guo)
2 Mar 15, 2022
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58 Dec 18, 2022
![A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]](https://user-images.githubusercontent.com/33194443/104581604-2592cb00-56a2-11eb-9610-5eaa0afb6e1f.png)
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