32 lines
1.3 KiB
Markdown
32 lines
1.3 KiB
Markdown
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# Traffic Lights Object Detector using YOLOv8 neural network
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<div align="center">
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<a href="https://dev.to/andreygermanov/a-practical-introduction-to-object-detection-with-yolov8-neural-network-3n8c">
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<img src="https://res.cloudinary.com/practicaldev/image/fetch/s--mZ1E0vOa--/c_imagga_scale,f_auto,fl_progressive,h_420,q_auto,w_1000/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/n2auv9i8405cgnxhru40.png"/>
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</a>
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</div>
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The source code for [this](https://dev.to/andreygermanov/a-practical-introduction-to-object-detection-with-yolov8-neural-network-3n8c) article.
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This is a web interface to [YOLOv8 object detection neural network](https://ultralytics.com/yolov8)
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implemented on [Python](https://www.python.org) that uses a model to detect traffic lights and road signs on images.
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## Install
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* Clone this repository: `git clone git@github.com:AndreyGermanov/yolov8_pytorch_python.git`
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* Go to the root of cloned repository
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* Install dependencies by running `pip3 install -r requirements.txt`
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## Run
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Execute:
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```
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python3 object_detector.py
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```
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It will start a webserver on http://localhost:8080. Use any web browser to open the web interface.
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Using the interface you can upload the image to the object detector and see bounding boxes of all objects detected on it.
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