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<h1 align="center">yolov5.js</h1> <p align="center"> <img width="200" src="https://yolov5js-images.s3.eu-central-1.amazonaws.com/yolov5js-logo.png" alt="Logo"> </p>

<div align="center">Install</div>

npm install --save yolov5js

<div align="center">Example</div>

Want to use yolov5js in your project but don't know how? Take a peek at our sample React app or run it in codesandbox.

<div align="center">Convert</div>

# clone YOLOv5 repository
git clone https://github.com/ultralytics/yolov5.git
cd yolov5

# create python virtual environment [recommended]
virtualenv venv
source venv/bin/activate

# install dependencies
pip install -r requirements.txt
pip install tensorflowjs

# convert model to tensorflow.js format
python export.py --weights yolov5s.pt --include tfjs

<div align="center">Zoo</div>

Use and share pretrained YOLOv5 tensorflow.js models with yolov5.js-zoo.

<div align="center">Documentation</div>

Our proper documentation are still under construction 🚧. We are working on it really hard.

<details open> <summary>Load pre-trained model from zoo</summary>

import {load, YOLO_V5_N_COCO_MODEL_CONFIG} from 'yolov5js'

const model = await load(YOLO_V5_N_COCO_MODEL_CONFIG)

</details> <details open> <summary>Load custom model from file</summary>

import {load, ModelConfig} from 'yolov5js'
    
const uploadJSONInput = document.getElementById('upload-json');
const uploadWeightsInput = document.getElementById('upload-weights');
    
const config = { source: [uploadJSONInput.files[0], uploadWeightsInput.files[0]] }
const model = await load(config)

</details>

<div align="center">Kudos</div>

Kudos to ultralytics team as well as all other open-source contributors for building YOLOv5 project, and making it all possible.

<div align="center">License</div>

Project is freely distributable under the terms of the MIT license.